12.1.4.10 Pansharpening, Fusion of Aerial Images

Chapter Contents (Back)
Sensor Fusion. Cartography. Remote Sensing. Pansharpening. Pan-Sharpening. Pan Sharpening. Fusion of different resolution images initially designed to display color (Multi-spectral) images at a higher resolution than they really are. And for higher resolution analysis.
See also Evaluation, Quality Assissment Pansharpening.
See also Sharpening, Unsharp Masking.
See also Pansharpening for Hyperspectral Data. Can be similar to hyperspectral techniques:
See also Super Resolution for Hyperspectral Data.

Gross, H.N., Schott, J.R.,
Application of Spectral Mixture Analysis and Image Fusion Techniques for Image Sharpening,
RSE(63), No. 2, February 1998, pp. 85-94. 9801
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Ballester, C.[Coloma], Caselles, V.[Vicent], Igual, L.[Laura], Verdera, J.[Joan], Rougé, B.[Bernard],
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IJCV(69), No. 1, August 2006, pp. 43-58.
Springer DOI 0606
Increase resolution using pan image and lower resolution multi-spectral iamge. BibRef

Aiazzi, B.[Bruno], Alparone, L.[Luciano], Baronti, S.[Stefano], Garzelli, A., Selva, M.[Massimo],
MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery,
PhEngRS(72), No. 5, May 2006, pp. 591-596.
DOI Link 0610
A multiresolution framework for merging a multispectral image having an arbitrary number of bands with a higher-resolution panchromatic observation. BibRef

Garzelli, A.[Andrea], Nencini, F.[Filippo],
Panchromatic sharpening of remote sensing images using a multiscale Kalman filter,
PR(40), No. 12, December 2007, pp. 3568-3577.
Elsevier DOI 0709
Pan-sharpening; Multiresolution image fusion; "A trous" wavelet transform; Multiscale Kalman filter BibRef

Garzelli, A., Nencini, F., Capobianco, L.,
Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images,
GeoRS(46), No. 1, January 2008, pp. 228-236.
IEEE DOI 0712
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Garzelli, A.,
Pansharpening of Multispectral Images Based on Nonlocal Parameter Optimization,
GeoRS(53), No. 4, April 2015, pp. 2096-2107.
IEEE DOI 1502
geophysical image processing BibRef

Aiazzi, B.[Bruno], Alparone, L.[Luciano], Baronti, S.[Stefano], Pippi, I.[Ivan], Selva, M.[Massimo],
Generalized Laplacian Pyramid-Based Fusion of MS + P Image Data with Spectral Distortion Minimization,
PCV02(B: 3). 0305
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Aiazzi, B., Baronti, S., Selva, M.,
Improving Component Substitution Pansharpening Through Multivariate Regression of MS+Pan Data,
GeoRS(45), No. 10, October 2007, pp. 3230-3239.
IEEE DOI 0711
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Shah, V.P., Younan, N.H., King, R.L.,
An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets,
GeoRS(46), No. 5, May 2008, pp. 1323-1335.
IEEE DOI 0804
BibRef

Fasbender, D., Radoux, J., Bogaert, P.,
Bayesian Data Fusion for Adaptable Image Pansharpening,
GeoRS(46), No. 6, June 2008, pp. 1847-1857.
IEEE DOI 0711
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Lee, J., Lee, C.,
Fast and Efficient Panchromatic Sharpening,
GeoRS(48), No. 1, January 2010, pp. 155-163.
IEEE DOI 1001
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Mahyari, A.G.[A. Golibagh], Yazdi, M.,
Panchromatic and Multispectral Image Fusion Based on Maximization of Both Spectral and Spatial Similarities,
GeoRS(49), No. 6, June 2011, pp. 1976-1985.
IEEE DOI 1106
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Choi, K., Kim, C., Kang, M.H., Ra, J.B.,
Resolution Improvement of Infrared Images Using Visible Image Information,
SPLetters(18), No. 10, October 2011, pp. 611-614.
IEEE DOI 1109
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Li, S., Yang, B.,
A New Pan-Sharpening Method Using a Compressed Sensing Technique,
GeoRS(49), No. 2, February 2011, pp. 738-746.
IEEE DOI 1102
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Saeedi, J.[Jamal], Faez, K.[Karim],
A new pan-sharpening method using multiobjective particle swarm optimization and the shiftable contourlet transform,
PandRS(66), No. 3, May 2011, pp. 365-381.
Elsevier DOI 1103
Pan-sharpening; Shiftable contourlet transform; Multiobjective particle swarm optimization BibRef

Massip, P., Blanc, P., Wald, L.,
A Method to Better Account for Modulation Transfer Functions in ARSIS-Based Pansharpening Methods,
GeoRS(50), No. 3, March 2012, pp. 800-808.
IEEE DOI 1203
BibRef

Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng], Gong, W.[Wei], Zhang, H.Y.[Hong-Yan],
Adjustable Model-Based Fusion Method for Multispectral and Panchromatic Images,
SMC-B(42), No. 6, December 2012, pp. 1693-1704.
IEEE DOI 1212
BibRef

Gao, F., Kustas, W., Anderson, M.,
A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land,
RS(4), No. 11, November 2012, pp. 3287-3319.
DOI Link 1211
BibRef

Johnson, B., Tateishi, R., Hoan, N.,
Satellite Image Pansharpening Using a Hybrid Approach for Object-Based Image Analysis,
IJGI(1), No. 3, 2012, pp. 228-241.
DOI Link 1211
BibRef

Ellmauthaler, A.[Andreas], Pagliari, C.L.[Carla L.], da Silva, E.A.B.[Eduardo A.B.],
Multiscale Image Fusion Using the Undecimated Wavelet Transform With Spectral Factorization and Nonorthogonal Filter Banks,
IP(22), No. 3, March 2013, pp. 1005-1017.
IEEE DOI 1302
BibRef

Ellmauthaler, A.[Andreas], da Silva, E.A.B.[Eduardo A.B.], Pagliari, C.L.[Carla L.], Neves, S.R.[Sergio R.],
Infrared-visible image fusion using the undecimated wavelet transform with spectral factorization and target extraction,
ICIP12(2661-2664).
IEEE DOI 1302
BibRef

Alidoost, F.[Fakhereh], Mobasheri, M.R.[Mohammad R.], Abkar, A.A.[Ali A.],
Introducing a Method for Spectral Enrichment of the High Spatial Resolution Images,
PFG(2013), No. 1, 2013, pp. 31-41.
DOI Link 1303
BibRef

Ribeiro Sales, M.H., Souza, C.M., Kyriakidis, P.C.,
Fusion of MODIS Images Using Kriging With External Drift,
GeoRS(51), No. 4, April 2013, pp. 2250-2259.
IEEE DOI 1304
Different bands of MODIS have different resolutions. BibRef

Zhu, X.X.[Xiao Xiang], Bamler, R.[Richard],
A Sparse Image Fusion Algorithm With Application to Pan-Sharpening,
GeoRS(51), No. 5, May 2013, pp. 2827-2836.
IEEE DOI 1305

See also Super-Resolution Power and Robustness of Compressive Sensing for Spectral Estimation With Application to Spaceborne Tomographic SAR. BibRef

Fang, F.M.[Fa-Ming], Li, F.[Fang], Shen, C.M.[Chao-Min], Zhang, G.X.[Gui-Xu],
A Variational Approach for Pan-Sharpening,
IP(22), No. 7, 2013, pp. 2822-2834.
IEEE DOI best pan-sharpened result; split Bregman algorithm; variational approach 1307
BibRef

Tang, S.Z.[Si-Zhang], Fang, F.M.[Fa-Ming], Zhang, G.X.[Gui-Xu],
Variational approach for multi-source image fusion,
IET-IPR(9), No. 2, 2015, pp. 134-141.
DOI Link 1503
gradient methods BibRef

Unni, R.K.[Ravi Krishnan], Jiji, C.V.,
Fusion of Multispectral and Panchromatic Images Based on the Nonsubsampled Contourlet Transform,
IJIG(13), No. 03, 2013, pp. 1350010.
DOI Link 1309
BibRef

Aiazzi, B.[Bruno], Baronti, S.[Stefano], Selva, M.[Massimo], Alparone, L.[Luciano],
Bi-cubic interpolation for shift-free pan-sharpening,
PandRS(86), No. 1, 2013, pp. 65-76.
Elsevier DOI 1312
Digital filtering BibRef

Kang, X., Li, S., Benediktsson, J.A.,
Pansharpening With Matting Model,
GeoRS(52), No. 8, August 2014, pp. 5088-5099.
IEEE DOI 1403
Image coding
See also Spectral-Spatial Hyperspectral Image Classification With Edge-Preserving Filtering. BibRef

Li, X., Ling, F., Du, Y., Zhang, Y.,
Spatially Adaptive Superresolution Land Cover Mapping With Multispectral and Panchromatic Images,
GeoRS(52), No. 5, May 2014, pp. 2810-2823.
IEEE DOI 1403
Panchromatic (PAN) image BibRef

Ling, F., Li, X., Xiao, F., Du, Y.,
Superresolution Land Cover Mapping Using Spatial Regularization,
GeoRS(52), No. 7, July 2014, pp. 4424-4439.
IEEE DOI 1403
Data models BibRef

Buades, A.[Antoni], Coll, B.[Bartomeu], Duran, J.[Joan], Sbert, C.[Catalina],
Implementation of Nonlocal Pansharpening Image Fusion,
IPOL(2014), No. 2014, pp. 1-15.
DOI Link 1404
Code, Pansharpening.
See also Nonlocal Variational Model for Pansharpening Image Fusion, A. BibRef

Duran, J.[Joan], Buades, A.[Antoni], Coll, B.[Bartomeu], Sbert, C.[Catalina],
A Nonlocal Variational Model for Pansharpening Image Fusion,
SIIMS(7), No. 2, 2014, pp. 761-796.
DOI Link 1405

See also Implementation of Nonlocal Pansharpening Image Fusion. BibRef

Johnson, B.[Brian],
Effects of Pansharpening on Vegetation Indices,
IJGI(3), No. 2, 2014, pp. 507-522.
DOI Link 1405
BibRef

Aly, H.A., Sharma, G.,
A Regularized Model-Based Optimization Framework for Pan-Sharpening,
IP(23), No. 6, June 2014, pp. 2596-2608.
IEEE DOI 1406
Image sensors BibRef

Aslantas, V., Bendes, E., Kurban, R., Toprak, A.N.,
New optimised region-based multi-scale image fusion method for thermal and visible images,
IET-IPR(8), No. 5, May 2014, pp. 289-299.
DOI Link 1407
BibRef

Saeidi, V., Pradhan, B., Idrees, M.O., Latif, Z.A.[Z. Abd],
Fusion of Airborne LiDAR With Multispectral SPOT 5 Image for Enhancement of Feature Extraction Using Dempster-Shafer Theory,
GeoRS(52), No. 10, October 2014, pp. 6017-6025.
IEEE DOI 1407
Accuracy BibRef

Wang, P., Gao, F., Masek, J.G.,
Operational Data Fusion Framework for Building Frequent Landsat-Like Imagery,
GeoRS(52), No. 11, November 2014, pp. 7353-7365.
IEEE DOI 1407
Clouds BibRef

Xu, Q., Li, B., Zhang, Y., Ding, L.,
High-Fidelity Component Substitution Pansharpening by the Fitting of Substitution Data,
GeoRS(52), No. 11, November 2014, pp. 7380-7392.
IEEE DOI 1407
Image fusion BibRef

Yang, J.H.[Jing-Hui], Zhang, J.X.[Ji-Xian], Huang, G.M.[Guo-Man],
A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer,
RS(6), No. 7, 2014, pp. 6039-6063.
DOI Link 1408
BibRef

Liu, Q.J.[Qing-Jie], Wang, Y.H.[Yun-Hong], Zhang, Z.X.[Zhao-Xiang], Liu, L.[Lining],
Pan-sharpening based on weighted red black wavelets,
IET-IPR(8), No. 8, August 2014, pp. 477-488.
DOI Link 1410
BibRef
Earlier:
Pan-sharpening using weighted red-black wavelet,
ICPR12(1908-1911).
WWW Link. 1302
BibRef
Earlier: A1, A4, A2, A3:
Locally linear embedding based example learning for pan-sharpening,
ICPR12(1928-1931).
WWW Link. 1302
image fusion BibRef

Dong, W.H.[Wei-Hua], Li, X.[Xian'en], Lin, X.G.[Xiang-Guo], Li, Z.L.[Zhi-Lin],
A Bidimensional Empirical Mode Decomposition Method for Fusion of Multispectral and Panchromatic Remote Sensing Images,
RS(6), No. 9, 2014, pp. 8446-8467.
DOI Link 1410
BibRef

He, X.[Xiyan], Condat, L., Bioucas-Dias, J.M., Chanussot, J., Xia, J.[Junshi],
A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors,
IP(23), No. 9, September 2014, pp. 4160-4174.
IEEE DOI 1410
geophysical image processing BibRef

Vivone, G.[Gemine], Simões, M.[Miguel], Dalla Mura, M.[Mauro], Restaino, R.[Rocco], Bioucas-Dias, J.M.[José M.], Licciardi, G.A.[Giorgio A.], Chanussot, J.[Jocelyn],
Pansharpening Based on Semiblind Deconvolution,
GeoRS(53), No. 4, April 2015, pp. 1997-2010.
IEEE DOI 1502
Gaussian processes BibRef

Picone, D., Condat, L., Cotte, F., Dalla Mura, M.[Mauro],
Image Fusion and Reconstruction of Compressed Data: A Joint Approach,
ICIP18(878-882)
IEEE DOI 1809
Image coding, Optical imaging, Spatial resolution, Image reconstruction, Optical sensors, Image fusion, Image fusion, optical devices BibRef

Kallel, A.,
MTF-Adjusted Pansharpening Approach Based on Coupled Multiresolution Decompositions,
GeoRS(53), No. 6, June 2015, pp. 3124-3145.
IEEE DOI 1503
discrete wavelet transforms BibRef

Shi, C.[Cheng], Liu, F.[Fang], Li, L.L.[Ling-Ling], Jiao, L.C.[Li-Cheng], Duan, Y.P.[Yi-Ping], Wang, S.[Shuang],
Learning Interpolation via Regional Map for Pan-Sharpening,
GeoRS(53), No. 6, June 2015, pp. 3417-3431.
IEEE DOI 1503
geophysical image processing BibRef

Abdullah, S.M.U., Rehman, N.U., Khan, M.M., Mandic, D.P.,
A Multivariate Empirical Mode DecompositionBased Approach to Pansharpening,
GeoRS(53), No. 7, July 2015, pp. 3974-3984.
IEEE DOI 1503
Context BibRef

Yin, H.T.[Hai-Tao], Li, S.T.[Shu-Tao],
Pansharpening With Multiscale Normalized Nonlocal Means Filter: A Two-Step Approach,
GeoRS(53), No. 10, October 2015, pp. 5734-5745.
IEEE DOI 1509
filters BibRef

Shu, Y.[Yang], Tang, H.[Hong], Li, J.[Jing], Mao, T.[Ting], He, S.[Shi], Gong, A.[Adu], Chen, Y.H.[Yun-Hao], Du, H.Y.[Hong-Yue],
Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images by Combining the HDP and IBP on Multiple Scenes,
GeoRS(53), No. 11, November 2015, pp. 6148-6162.
IEEE DOI 1509
Bayes methods BibRef

Bostater, C.[Charles],
Optimal spectral image fusion for detection of shoreline targets,
SPIE(Newsroom), November 10, 2015
DOI Link 1512
Spectral-spatial sharpening of images is achieved by numerically embedding line targets in obtained imagery, and by minimizing the differences between high-spatial-resolution and observed spectral signatures. BibRef

Yokoya, N.[Naoto],
Texture-Guided Multisensor Superresolution for Remotely Sensed Images,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Lottering, R.[Romano], Mutanga, O.[Onisimo],
Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa,
PandRS(112), No. 1, 2016, pp. 13-22.
Elsevier DOI 1602
Gonipterus scutellatus BibRef

Li, H.[Hui], Jing, L.H.[Lin-Hai], Wang, L.M.[Li-Ming], Cheng, Q.M.[Qiu-Ming],
Improved Pansharpening with Un-Mixing of Mixed MS Sub-Pixels near Boundaries between Vegetation and Non-Vegetation Objects,
RS(8), No. 2, 2016, pp. 83.
DOI Link 1603
BibRef

Zhang, H.K.[Hankui K.], Roy, D.P.[David P.],
Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening,
RS(8), No. 3, 2016, pp. 180.
DOI Link 1604
BibRef

Yan, L.[Lin], Roy, D.P.[David P.], Zhang, H.[Hankui], Li, J.[Jian], Huang, H.Y.[Hai-Yan],
An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery,
RS(8), No. 6, 2016, pp. 520.
DOI Link 1608
BibRef

Hou, L.[Likun], Zhang, X.Q.[Xiao-Qun],
Pansharpening Image Fusion Using Cross-Channel Correlation: A Framelet-Based Approach,
JMIV(55), No. 1, May 2016, pp. 36-49.
WWW Link. 1604
BibRef

Ghahremani, M., Ghassemian, H.,
A Compressed-Sensing-Based Pan-Sharpening Method for Spectral Distortion Reduction,
GeoRS(54), No. 4, April 2016, pp. 2194-2206.
IEEE DOI 1604
Dictionaries BibRef

Liu, P., Xiao, L., Zhang, J., Naz, B.,
Spatial-Hessian-Feature-Guided Variational Model for Pan-Sharpening,
GeoRS(54), No. 4, April 2016, pp. 2235-2253.
IEEE DOI 1604
Algorithm design and analysis BibRef

Zhu, X.X., Grohnfeldt, C., Bamler, R.,
Exploiting Joint Sparsity for Pansharpening: The J-SparseFI Algorithm,
GeoRS(54), No. 5, May 2016, pp. 2664-2681.
IEEE DOI 1604
compressed sensing BibRef

Restaino, R., Vivone, G., Dalla Mura, M., Chanussot, J.,
Fusion of Multispectral and Panchromatic Images Based on Morphological Operators,
IP(25), No. 6, June 2016, pp. 2882-2895.
IEEE DOI 1605
Algorithm design and analysis BibRef

Frantz, D., Stellmes, M., Röder, A., Udelhoven, T., Mader, S., Hill, J.,
Improving the Spatial Resolution of Land Surface Phenology by Fusing Medium- and Coarse-Resolution Inputs,
GeoRS(54), No. 7, July 2016, pp. 4153-4164.
IEEE DOI 1606
Earth BibRef

Masi, G.[Giuseppe], Cozzolino, D.[Davide], Verdoliva, L.[Luisa], Scarpa, G.[Giuseppe],
Pansharpening by Convolutional Neural Networks,
RS(8), No. 7, 2016, pp. 594.
DOI Link 1608
BibRef

Mao, T., Tang, H., Wu, J., Jiang, W., He, S., Shu, Y.,
A Generalized Metaphor of Chinese Restaurant Franchise to Fusing Both Panchromatic and Multispectral Images for Unsupervised Classification,
GeoRS(54), No. 8, August 2016, pp. 4594-4604.
IEEE DOI 1608
geophysical image processing BibRef

Lu, H.Y.[Hong-Yang], Wei, J.B.[Jing-Bo], Wang, L.[Lizhe], Liu, P.[Peng], Liu, Q.[Qiegen], Wang, Y.H.[Yu-Hao], Deng, X.H.[Xiao-Hua],
Reference Information Based Remote Sensing Image Reconstruction with Generalized Nonconvex Low-Rank Approximation,
RS(8), No. 6, 2016, pp. 499.
DOI Link 1608
BibRef

Wang, Q.M.[Qun-Ming], Shi, W.Z.[Wen-Zhong], Atkinson, P.M.[Peter M.],
Area-to-point regression kriging for pan-sharpening,
PandRS(114), No. 1, 2016, pp. 151-165.
Elsevier DOI 1604

See also Spatiotemporal Subpixel Mapping of Time-Series Images.
See also Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class. Downscaling BibRef

Restaino, R.[Rocco], Mura, M.D.[Mauro Dalla], Vivone, G.[Gemine], Chanussot, J.[Jocelyn],
Context-Adaptive Pansharpening Based on Image Segmentation,
GeoRS(55), No. 2, February 2017, pp. 753-766.
IEEE DOI 1702
BibRef
Earlier: A1, A3, A2, A4:
A Pansharpening Algorithm Based on Morphological Filters,
ISMM15(98-109).
Springer DOI 1506
BibRef
Earlier: A1, A3, A1, A4:
Context-Adaptive Pansharpening Based on Binary Partition Tree Segmentation,
ICIP14(3924-3928)
IEEE DOI 1502
Laplace equations. Estimation
See also Binary Partition Trees-Based Robust Adaptive Hyperspectral RX Anomaly Detection. BibRef

Gajbhar, S.S.[Shrishail S.], Joshi, M.V.[Manjunath V.],
Design of complex adaptive multiresolution directional filter bank and application to pansharpening,
SIViP(11), No. 2, February 2017, pp. 259-266.
WWW Link. 1702
BibRef

Duran, J., Buades, A., Coll, B., Sbert, C., Blanchet, G.,
A survey of pansharpening methods with a new band-decoupled variational model,
PandRS(125), No. 1, 2017, pp. 78-105.
Elsevier DOI 1703
Remote sensing BibRef

Yang, Y.[Yong], Wan, W.G.[Wei-Guo], Huang, S.Y.[Shu-Ying], Lin, P.[Pan], Que, Y.[Yue],
A Novel Pan-Sharpening Framework Based on Matting Model and Multiscale Transform,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Xie, B.[Bin], Zhang, H.K.[Hankui K.], Huang, B.[Bo],
Revealing Implicit Assumptions of the Component Substitution Pansharpening Methods,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Yin, H.,
A Joint Sparse and Low-Rank Decomposition for Pansharpening of Multispectral Images,
GeoRS(55), No. 6, June 2017, pp. 3545-3557.
IEEE DOI 1706
Dictionaries, Distortion, Frequency response, Satellites, Sparse matrices, Spatial resolution, Details injection (DI), low-rank decomposition, multispectral image, panchromatic (PAN) image, pansharpening, sparse, decomposition BibRef

Grochala, A.[Aleksandra], Kedzierski, M.[Michal],
A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Rahaman, K.R.[Khan Rubayet], Hassan, Q.K.[Quazi K.], Ahmed, M.R.[M. Razu],
Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents,
IJGI(6), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Li, Z.B.[Zhong-Bin], Zhang, H.K.[Hankui K.], Roy, D.P.[David P.], Yan, L.[Lin], Huang, H.Y.[Hai-Yan], Li, J.[Jian],
Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Alparone, L., Garzelli, A., Vivone, G.,
Intersensor Statistical Matching for Pansharpening: Theoretical Issues and Practical Solutions,
GeoRS(55), No. 8, August 2017, pp. 4682-4695.
IEEE DOI 1708
Histograms, Instruments, Multiresolution analysis, Remote sensing, Satellites, Spatial resolution, multisensor systems, optical transfer functions, BibRef

Brodu, N.[Nicolas],
Super-Resolving Multiresolution Images With Band-Independent Geometry of Multispectral Pixels,
GeoRS(55), No. 8, August 2017, pp. 4610-4617.
IEEE DOI 1708
Data models, Earth, Geometry, Remote sensing, Satellites, Spatial resolution, Image enhancement, image resolution, multispectral imaging. BibRef

Choi, J.[Jaewan], Kim, G.[Guhyeok], Park, N.[Nyunghee], Park, H.[Honglyun], Choi, S.[Seokkeun],
A Hybrid Pansharpening Algorithm of VHR Satellite Images that Employs Injection Gains Based on NDVI to Reduce Computational Costs,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Park, H.[Honglyun], Choi, J.[Jaewan], Park, N.[Nyunghee], Choi, S.[Seokkeun],
Sharpening the VNIR and SWIR Bands of Sentinel-2A Imagery through Modified Selected and Synthesized Band Schemes,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Saxena, N.[Nidhi], Sharma, K.K.[Kamalesh K.],
Pansharpening approach using Hilbert vibration decomposition,
IET-IPR(11), No. 12, Decmeber 2017, pp. 1152-1162.
DOI Link 1712
BibRef

Zheng, Y.L.[Ya-Lan], Dai, Q.L.[Qin-Ling], Tu, Z.G.[Zhi-Gang], Wang, L.G.[Lei-Guang],
Guided Image Filtering-Based Pan-Sharpening Method: A Case Study of GaoFen-2 Imagery,
IJGI(6), No. 12, 2017, pp. xx-yy.
DOI Link 1801
BibRef

Vivone, G., Restaino, R., Chanussot, J.,
A Regression-Based High-Pass Modulation Pansharpening Approach,
GeoRS(56), No. 2, February 2018, pp. 984-996.
IEEE DOI 1802
image fusion, image resolution, remote sensing, GeoEye-1 sensors, PAN image, Ple´iades sensors, WorldView-2 sensors, remote sensing BibRef

Liu, P., Xiao, L., Li, T.,
A Variational Pan-Sharpening Method Based on Spatial Fractional-Order Geometry and Spectral-Spatial Low-Rank Priors,
GeoRS(56), No. 3, March 2018, pp. 1788-1802.
IEEE DOI 1804
geophysical image processing, image fusion, image resolution, image texture, optimisation, remote sensing, weighted nuclear norm (WNN) BibRef

Xing, Y., Wang, M., Yang, S., Zhang, K.,
Pansharpening With Multiscale Geometric Support Tensor Machine,
GeoRS(56), No. 5, May 2018, pp. 2503-2517.
IEEE DOI 1805
Distortion, Image color analysis, Spatial resolution, Support vector machines, Tensile stress, Transforms, pansharpening BibRef

Vivone, G., Restaino, R., Chanussot, J.,
Full Scale Regression-Based Injection Coefficients for Panchromatic Sharpening,
IP(27), No. 7, July 2018, pp. 3418-3431.
IEEE DOI 1805
Closed-form solutions, Estimation, Iterative methods, Multiresolution analysis, Satellites, Spatial resolution, remote sensing BibRef

Zhang, Z.Y.[Zi-Yao], Huang, T.Z.[Ting-Zhu], Deng, L.J.[Liang-Jian], Huang, J.[Jie], Zhao, X.L.[Xi-Le], Zheng, C.C.[Chao-Chao],
A Framelet-Based Iterative Pan-Sharpening Approach,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
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Saxena, N.[Nidhi], Sharma, K.K.[Kamalesh K.],
Pansharpening scheme using filtering in two-dimensional discrete fractional Fourier transform,
IET-IPR(12), No. 6, June 2018, pp. 1013-1019.
DOI Link 1805
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Zhao, C., Gao, X., Emery, W.J., Wang, Y., Li, J.,
An Integrated Spatio-Spectral-Temporal Sparse Representation Method for Fusing Remote-Sensing Images With Different Resolutions,
GeoRS(56), No. 6, June 2018, pp. 3358-3370.
IEEE DOI 1806
Data integration, Image fusion, MODIS, Remote sensing, Sensors, Spatial resolution, Heterogeneous land surface monitoring, temporal features BibRef

Ping, B.[Bo], Meng, Y.S.[Yun-Shan], Su, F.Z.[Fen-Zhen],
An Enhanced Linear Spatio-Temporal Fusion Method for Blending Landsat and MODIS Data to Synthesize Landsat-Like Imagery,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
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Deng, L.J., Vivone, G., Guo, W., Dalla Mura, M., Chanussot, J.,
A Variational Pansharpening Approach Based on Reproducible Kernel Hilbert Space and Heaviside Function,
IP(27), No. 9, September 2018, pp. 4330-4344.
IEEE DOI 1807
BibRef
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IEEE DOI 1803
Hilbert spaces, geophysical image processing, image fusion, image resolution, optimisation, remote sensing, sparse model. Distortion, Image edge detection, Kernel, Multiresolution analysis, Sensors, Spatial resolution BibRef

Cui, J.T.[Jin-Tian], Zhang, X.[Xin], Luo, M.Y.[Mu-Ying],
Combining Linear Pixel Unmixing and STARFM for Spatiotemporal Fusion of Gaofen-1 Wide Field of View Imagery and MODIS Imagery,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
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Garzelli, A.[Andrea], Aiazzi, B.[Bruno], Alparone, L.[Luciano], Lolli, S.[Simone], Vivone, G.[Gemine],
Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
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Scarpa, G., Vitale, S., Cozzolino, D.,
Target-Adaptive CNN-Based Pansharpening,
GeoRS(56), No. 9, September 2018, pp. 5443-5457.
IEEE DOI 1809
Training, Sensors, Spatial resolution, Transforms, Protocols, Remote sensing, Convolutional neural networks (CNN), Urban areas BibRef

Ciotola, M.[Matteo], Scarpa, G.[Giuseppe],
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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He, G.Q.[Gui-Qing], Xing, S.Y.[Si-Yuan], Xia, Z.Q.A.[Zhao-Qi-Ang], Huang, Q.Q.[Qing-Qing], Fan, J.P.[Jian-Ping],
Panchromatic and multi-spectral image fusion for new satellites based on multi-channel deep model,
RealTimeIP(14), No. 1, January 2018, pp. 933-946.
Springer DOI 1809
BibRef

Zhong, D.T.[De-Tang], Zhou, F.[Fuqun],
A Prediction Smooth Method for Blending Landsat and Moderate Resolution Imagine Spectroradiometer Images,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
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Lee, J.[Jacky], Cardille, J.A.[Jeffrey A.], Coe, M.T.[Michael T.],
BULC-U: Sharpening Resolution and Improving Accuracy of Land-Use/Land-Cover Classifications in Google Earth Engine,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
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Xing, Y.H.[Ying-Hui], Wang, M.[Min], Yang, S.Y.[Shu-Yuan], Jiao, L.C.[Li-Cheng],
Pan-sharpening via deep metric learning,
PandRS(145), 2018, pp. 165-183.
Elsevier DOI 1810
Pan-sharpening, Deep metric learning, Stacked Sparse AutoEncoders, Geometric multi-manifold embedding BibRef

Gogineni, R.[Rajesh], Chaturvedi, A.[Ashvini],
Sparsity inspired pan-sharpening technique using multi-scale learned dictionary,
PandRS(146), 2018, pp. 360-372.
Elsevier DOI 1812
Pan-sharpening, Sparse representation, Dictionary learning, Wavelet transform, Multi-scale learned dictionary BibRef

Vivone, G., Addesso, P., Restaino, R., Dalla Mura, M., Chanussot, J.,
Pansharpening Based on Deconvolution for Multiband Filter Estimation,
GeoRS(57), No. 1, January 2019, pp. 540-553.
IEEE DOI 1901
Iron, Sensors, Feature extraction, Deconvolution, Spatial resolution, Image sensors, Estimation, Deconvolution, image fusion, remote sensing BibRef

Zhang, K.[Kai], Wang, M.[Min], Yang, S.Y.[Shu-Yuan], Jiao, L.C.[Li-Cheng],
Convolution Structure Sparse Coding for Fusion of Panchromatic and Multispectral Images,
GeoRS(57), No. 2, February 2019, pp. 1117-1130.
IEEE DOI 1901
Image coding, Convolution, Dictionaries, Image fusion, Correlation, Image restoration, Image resolution, structure sparsity BibRef

Xing, Y.H.[Ying-Hui], Yang, S.Y.[Shu-Yuan], Feng, Z.X.[Zhi-Xi], Jiao, L.C.[Li-Cheng],
Dual-Collaborative Fusion Model for Multispectral and Panchromatic Image Fusion,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI 2112
Feature extraction, Image fusion, Task analysis, Collaboration, Image reconstruction, Remote sensing, Spatial resolution, remote sensing BibRef

Xing, Y.H.[Ying-Hui], Yang, S.Y.[Shu-Yuan], Zhang, Y.[Yan], Zhang, Y.N.[Yan-Ning],
Learning Spectral Cues for Multispectral and Panchromatic Image Fusion,
IP(31), 2022, pp. 6964-6975.
IEEE DOI 2212
Feature extraction, Image fusion, Pansharpening, Task analysis, Spatial resolution, Remote sensing, Modulation, Image fusion, generative adversarial networks BibRef

Paris, C., Bioucas-Dias, J., Bruzzone, L.,
A Novel Sharpening Approach for Superresolving Multiresolution Optical Images,
GeoRS(57), No. 3, March 2019, pp. 1545-1560.
IEEE DOI 1903
geophysical image processing, image colour analysis, image denoising, image resolution, image sequences, superresolution BibRef

Choi, J.[Jaewan], Park, H.[Honglyun], Seo, D.[Doochun],
Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
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Xia, H.P.[Hai-Ping], Chen, Y.H.[Yun-Hao], Quan, J.L.[Jin-Ling], Li, J.[Jing],
Object-Based Window Strategy in Thermal Sharpening,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
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Liu, J.[Junmin], Ma, J.[Jing], Fei, R.R.[Rong-Rong], Li, H.R.[Hui-Rong], Zhang, J.S.[Jiang-She],
Enhanced Back-Projection as Postprocessing for Pansharpening,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
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Zhang, K.[Kai], Zhang, F.[Feng], Yang, S.Y.[Shu-Yuan],
Fusion of Multispectral and Panchromatic Images via Spatial Weighted Neighbor Embedding,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
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Ye, F.[Fei], Guo, Y.[Yecai], Zhuang, P.X.[Pei-Xian],
Pan-sharpening via a gradient-based deep network prior,
SP:IC(74), 2019, pp. 322-331.
Elsevier DOI 1904
Pan-sharpening, Model-based optimization, Convolutional neural network, Gradient-based prior BibRef

Yin, H.,
PAN-Guided Cross-Resolution Projection for Local Adaptive Sparse Representation- Based Pansharpening,
GeoRS(57), No. 7, July 2019, pp. 4938-4950.
IEEE DOI 1907
Spatial resolution, Image reconstruction, Dictionaries, Signal resolution, Image sensors, Sensors, sparse representation BibRef

Wang, J., Yang, X., Zhu, R.,
Random Walks for Pansharpening in Complex Tight Framelet Domain,
GeoRS(57), No. 7, July 2019, pp. 5121-5134.
IEEE DOI 1907
Transforms, Hidden Markov models, Image fusion, Spatial resolution, Probability, Computational modeling, Image edge detection, random walks (RWs) BibRef

Li, W.[Wei], Jiang, J.[Jiale], Guo, T.[Tai], Zhou, M.[Meng], Tang, Y.[Yining], Wang, Y.[Ying], Zhang, Y.[Yu], Cheng, T.[Tao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Yao, X.[Xia],
Generating Red-Edge Images at 3 M Spatial Resolution by Fusing Sentinel-2 and Planet Satellite Products,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
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Zhang, Y., Liu, C., Sun, M., Ou, Y.,
Pan-Sharpening Using an Efficient Bidirectional Pyramid Network,
GeoRS(57), No. 8, August 2019, pp. 5549-5563.
IEEE DOI 1908
geophysical image processing, geophysical techniques, image resolution, remote sensing, pan-sharpened image, remote sensing BibRef

Gewali, U.B.[Utsav B.], Monteiro, S.T.[Sildomar T.], Saber, E.[Eli],
Spectral Super-Resolution with Optimized Bands,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
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Shen, H., Jiang, M., Li, J., Yuan, Q., Wei, Y., Zhang, L.,
Spatial-Spectral Fusion by Combining Deep Learning and Variational Model,
GeoRS(57), No. 8, August 2019, pp. 6169-6181.
IEEE DOI 1908
convolutional neural nets, feature extraction, geophysical image processing, image classification, image fusion, spatial-spectral fusion BibRef

Ulfarsson, M.O., Palsson, F., Mura, M.D.[M. Dalla], Sveinsson, J.R.,
Sentinel-2 Sharpening Using a Reduced-Rank Method,
GeoRS(57), No. 9, September 2019, pp. 6408-6420.
IEEE DOI 1909
Spatial resolution, Image sensors, Optimization, Thermal sensors, Remote sensing, Cyclic descent (CD), data fusion, image sharpening, superresolution BibRef

Vivone, G.,
Robust Band-Dependent Spatial-Detail Approaches for Panchromatic Sharpening,
GeoRS(57), No. 9, September 2019, pp. 6421-6433.
IEEE DOI 1909
Sensors, Spatial resolution, Multiresolution analysis, Estimation, Benchmark testing, Wavelet transforms, robust regression BibRef

Hu, J.[Jie], He, Z.[Zhi], Wu, J.[Jiemin],
Deep Self-Learning Network for Adaptive Pansharpening,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
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Vivone, G.[Gemine], Alparone, L.[Luciano], Garzelli, A.[Andrea], Lolli, S.[Simone],
Fast Reproducible Pansharpening Based on Instrument and Acquisition Modeling: AWLP Revisited,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
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Jing, Y.H.[Ying-Hong], Shen, H.F.[Huan-Feng], Li, X.H.[Xing-Hua], Guan, X.O.[Xia-Obin],
A Two-Stage Fusion Framework to Generate a Spatio-Temporally Continuous MODIS NDSI Product over the Tibetan Plateau,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
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Liu, P.F.[Peng-Fei],
Pansharpening with transform-based gradient transferring model,
IET-IPR(13), No. 13, November 2019, pp. 2614-2622.
DOI Link 1911
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Mao, T.[Ting], Tang, H.[Hong], Huang, W.[Wei],
Unsupervised Classification of Multispectral Images Embedded With a Segmentation of Panchromatic Images Using Localized Clusters,
GeoRS(57), No. 11, November 2019, pp. 8732-8744.
IEEE DOI 1911
Image segmentation, Spatial resolution, Remote sensing, Image color analysis, Spatial coherence, Fuses, unsupervised classification BibRef

Li, Z.Q.[Zhi-Qiang], Cheng, C.Q.[Cheng-Qi],
A CNN-Based Pan-Sharpening Method for Integrating Panchromatic and Multispectral Images Using Landsat 8,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
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Wang, D.[Dong], Li, Y.[Ying], Ma, L.[Li], Bai, Z.W.[Zong-Wen], Chan, J.C.W.[Jonathan Cheung-Wai],
Going Deeper with Densely Connected Convolutional Neural Networks for Multispectral Pansharpening,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
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Yilmaz, C.S.[Cigdem Serifoglu], Yilmaz, V.[Volkan], Gungor, O.[Oguz], Shan, J.[Jie],
Metaheuristic pansharpening based on symbiotic organisms search optimization,
PandRS(158), 2019, pp. 167-187.
Elsevier DOI 1912
Pansharpening, Symbiotic organisms search, Synthetic variable ratio, Metaheuristic algorithms, Image fusion BibRef

Khateri, M.[Mohammad], Shabanzade, F.[Fahim], Mirzapour, F.[Fardin],
Regularised IHS-based pan-sharpening approach using spectral consistency constraint and total variation,
IET-IPR(14), No. 1, January 2020, pp. 94-104.
DOI Link 1912
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Wang, Q., Shi, W., Atkinson, P.M.,
Information Loss-Guided Multi-Resolution Image Fusion,
GeoRS(58), No. 1, January 2020, pp. 45-57.
IEEE DOI 2001
Spatial resolution, Image fusion, Remote sensing, Earth, Coherence, Predictive models, Downscaling, information loss (IL) BibRef

Vitale, S.[Sergio], Scarpa, G.[Giuseppe],
A Detail-Preserving Cross-Scale Learning Strategy for CNN-Based Pansharpening,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
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Jia, D.[Duo], Song, C.Q.[Chang-Qing], Cheng, C.X.[Chang-Xiu], Shen, S.[Shi], Ning, L.X.[Li-Xin], Hui, C.[Chun],
A Novel Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions Using a Two-Stream Convolutional Neural Network,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
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Jia, D.[Duo], Cheng, C.X.[Chang-Xiu], Song, C.Q.[Chang-Qing], Shen, S.[Shi], Ning, L.X.[Li-Xin], Zhang, T.Y.[Tian-Yuan],
A Hybrid Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 1806
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Yang, Y.[Yong], Tu, W.[Wei], Huang, S.Y.[Shu-Ying], Lu, H.Y.[Hang-Yuan],
PCDRN: Progressive Cascade Deep Residual Network for Pansharpening,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
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Huang, W.[Wei], Feng, J.J.[Jing-Jing], Wang, H.[Hua], Sun, L.[Le],
A New Architecture of Densely Connected Convolutional Networks for Pan-Sharpening,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
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Phinzi, K.[Kwanele], Abriha, D.[Dávid], Bertalan, L.[László], Holb, I.[Imre], Szabó, S.[Szilárd],
Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
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Jiang, M.H.[Meng-Hui], Shen, H.F.[Huan-Feng], Li, J.[Jie], Yuan, Q.Q.[Qiang-Qiang], Zhang, L.P.[Liang-Pei],
A differential information residual convolutional neural network for pansharpening,
PandRS(163), 2020, pp. 257-271.
Elsevier DOI 2005
Pansharpening, RCNN, Differential information mapping, Auxiliary gradient BibRef

Huang, W.[Wei], Fei, X.[Xuan], Feng, J.J.[Jing-Jing], Wang, H.[Hua], Liu, Y.[Yan], Huang, Y.[Yao],
Pan-sharpening via multi-scale and multiple deep neural networks,
SP:IC(85), 2020, pp. 115850.
Elsevier DOI 2005
Deep neural network (DNN), Residual compensation, Multispectral image, Pan-sharpening BibRef

Kim, Y.[Yeseul], Kyriakidis, P.C.[Phaedon C.], Park, N.W.[No-Wook],
A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
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Fu, S.P.[Shi-Peng], Meng, W.H.[Wei-Hua], Jeon, G.G.[Gwang-Gil], Chehri, A.[Abdellah], Zhang, R.Z.[Rong-Zhu], Yang, X.M.[Xiao-Min],
Two-Path Network with Feedback Connections for Pan-Sharpening in Remote Sensing,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
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Liu, C.[Chi], Zhang, Y.J.[Yong-Jun], Wang, S.G.[Shu-Gen], Sun, M.W.[Ming-Wei], Ou, Y.J.[Yang-Jun], Wan, Y.[Yi], Liu, X.[Xiu],
Band-Independent Encoder-Decoder Network for Pan-Sharpening of Remote Sensing Images,
GeoRS(58), No. 7, July 2020, pp. 5208-5223.
IEEE DOI 2006
Decoding, Image resolution, Task analysis, Remote sensing, Feature extraction, Sensors, Transforms, Band-independent, pan-sharpening BibRef

Addesso, P., Vivone, G., Restaino, R., Chanussot, J.,
A Data-Driven Model-Based Regression Applied to Panchromatic Sharpening,
IP(29), 2020, pp. 7779-7794.
IEEE DOI 2007
Multivariate linear regression, injection models, pansharpening, image fusion, remote sensing BibRef

Vivone, G., Marano, S., Chanussot, J.,
Pansharpening: Context-Based Generalized Laplacian Pyramids by Robust Regression,
GeoRS(58), No. 9, September 2020, pp. 6152-6167.
IEEE DOI 2008
Robustness, Spatial resolution, Estimation, Multiresolution analysis, Discrete wavelet transforms, robust regression BibRef

Tian, X., Chen, Y., Yang, C., Gao, X., Ma, J.,
A Variational Pansharpening Method Based on Gradient Sparse Representation,
SPLetters(27), 2020, pp. 1180-1184.
IEEE DOI 2007
Pansharpening, variational model, gradient sparse representation, remote sensing BibRef

Zhou, C.S.[Chang-Sheng], Zhang, J.S.[Jiang-She], Liu, J.M.[Jun-Min], Zhang, C.X.[Chun-Xia], Fei, R.R.[Rong-Rong], Xu, S.[Shuang],
PercepPan: Towards Unsupervised Pan-Sharpening Based on Perceptual Loss,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
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Li, Z.B.[Zhong-Bin], Zhang, H.K.[Hankui K.], Roy, D.P.[David P.], Yan, L.[Lin], Huang, H.Y.[Hai-Yan],
Sharpening the Sentinel-2 10 and 20 m Bands to Planetscope-0 3 m Resolution,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
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Liu, J.M.[Jun-Min], Feng, Y.Q.[Yun-Qiao], Zhou, C.S.[Chang-Sheng], Zhang, C.X.[Chun-Xia],
PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
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Zhang, H.[Hao], Ma, J.Y.[Jia-Yi],
GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening,
PandRS(172), 2021, pp. 223-239.
Elsevier DOI 2101
Pansharpening, Gradient transformation prior, Deep learning, Image fusion, Remote sensing BibRef

Chen, B.[Bin], Li, J.[Jing], Jin, Y.F.[Yu-Fang],
Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
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Li, W.S.[Wei-Sheng], Liang, X.S.[Xue-Song], Dong, M.L.[Mei-Lin],
MDECNN: A Multiscale Perception Dense Encoding Convolutional Neural Network for Multispectral Pan-Sharpening,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
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Hu, J.W.[Jian-Wen], Hu, P.[Pei], Kang, X.D.[Xu-Dong], Zhang, H.[Hui], Fan, S.S.[Shao-Sheng],
Pan-Sharpening via Multiscale Dynamic Convolutional Neural Network,
GeoRS(59), No. 3, March 2021, pp. 2231-2244.
IEEE DOI 2103
Convolution, Feature extraction, Spatial resolution, Standards, Image reconstruction, Convolutional neural networks, weight generation network BibRef

Alparone, M., Nunziata, F., Estatico, C., Migliaccio, M.,
A Multichannel Data Fusion Method to Enhance the Spatial Resolution of Microwave Radiometer Measurements,
GeoRS(59), No. 3, March 2021, pp. 2213-2221.
IEEE DOI 2103
Spatial resolution, Microwave radiometry, Microwave measurement, Frequency measurement, Kernel, Microwave imaging, resolution enhancement BibRef

Wang, W.Q.[Wen-Qing], Zhou, Z.Q.[Zhi-Qiang], Liu, H.[Han], Xie, G.[Guo],
MSDRN: Pansharpening of Multispectral Images via Multi-Scale Deep Residual Network,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Ozcelik, F., Alganci, U., Sertel, E., Unal, G.,
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs,
GeoRS(59), No. 4, April 2021, pp. 3486-3501.
IEEE DOI 2104
Task analysis, Spatial resolution, Training, Standards, Sensors, Multiresolution analysis, AI, colorization, super-resolution (SR) BibRef

Qu, Y., Baghbaderani, R.K., Qi, H., Kwan, C.,
Unsupervised Pansharpening Based on Self-Attention Mechanism,
GeoRS(59), No. 4, April 2021, pp. 3192-3208.
IEEE DOI 2104
Spatial resolution, Image reconstruction, Sensors, Satellites, Image segmentation, Machine learning, Attention mechanism, unsupervised deep learning BibRef

Alcaras, E.[Emanuele], Parente, C.[Claudio], Vallario, A.[Andrea],
Automation of Pan-Sharpening Methods for Pléiades Images Using GIS Basic Functions,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
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Xu, H.[Han], Ma, J.Y.[Jia-Yi], Shao, Z.F.[Zhen-Feng], Zhang, H.[Hao], Jiang, J.J.[Jun-Junb], Guo, X.J.[Xiao-Jie],
SDPNet: A Deep Network for Pan-Sharpening With Enhanced Information Representation,
GeoRS(59), No. 5, May 2021, pp. 4120-4134.
IEEE DOI 2104
Feature extraction, Spatial resolution, Information representation, Data mining, Satellites, Training, pan-sharpening BibRef

Liu, Q.[Qin], Han, L.T.[Le-Tong], Tan, R.[Rui], Fan, H.F.[Hong-Fei], Li, W.Q.[Wei-Qi], Zhu, H.M.[Hong-Ming], Du, B.[Bowen], Liu, S.[Sicong],
Hybrid Attention Based Residual Network for Pansharpening,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Peng, J.Y.[Jin-Ye], Liu, L.[Lu], Wang, J.[Jun], Zhang, E.[Erlei], Zhu, X.[Xuan], Zhang, Y.Q.[Yong-Qin], Feng, J.[Jie], Jiao, L.C.[Li-Cheng],
PSMD-Net: A Novel Pan-Sharpening Method Based on a Multiscale Dense Network,
GeoRS(59), No. 6, June 2021, pp. 4957-4971.
IEEE DOI 2106
Feature extraction, Spatial resolution, Image reconstruction, Kernel, Frequency modulation, Remote sensing, residual learning BibRef

Benzenati, T.[Tayeb], Kallel, A.[Abdelaziz], Kessentini, Y.[Yousri],
Two Stages Pan-Sharpening Details Injection Approach Based on Very Deep Residual Networks,
GeoRS(59), No. 6, June 2021, pp. 4984-4992.
IEEE DOI 2106
Spatial resolution, Signal resolution, Estimation, Task analysis, Convolutional neural networks (CNNs), residual learning BibRef

Li, W.S.[Wei-Sheng], Xiang, M.H.[Ming-Hao], Liang, X.S.[Xue-Song],
MDCwFB: A Multilevel Dense Connection Network with Feedback Connections for Pansharpening,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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Li, W.S.[Wei-Sheng], Xiang, M.H.[Ming-Hao], Liang, X.S.[Xue-Song],
A Dense Encoder-Decoder Network with Feedback Connections for Pan-Sharpening,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Zhang, L.P.[Li-Ping], Li, W.S.[Wei-Sheng], Huang, H.F.[He-Fengo], Lei, D.J.[Da-Jiang],
A Pansharpening Generative Adversarial Network with Multilevel Structure Enhancement and a Multistream Fusion Architecture,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
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Wu, Y.Y.[Yuan-Yuan], Huang, M.X.[Meng-Xing], Li, Y.C.[Yu-Chun], Feng, S.L.[Si-Ling], Wu, D.[Di],
A Distributed Fusion Framework of Multispectral and Panchromatic Images Based on Residual Network,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Xie, Y.C.[Yu-Chen], Wu, W.[Wei], Yang, H.P.[Hai-Ping], Wu, N.[Ning], Shen, Y.[Ying],
Detail Information Prior Net for Remote Sensing Image Pansharpening,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Deng, L.J.[Liang-Jian], Vivone, G.[Gemine], Jin, C.[Cheng], Chanussot, J.[Jocelyn],
Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening,
GeoRS(59), No. 8, August 2021, pp. 6995-7010.
IEEE DOI 2108
Spatial resolution, Convolutional neural networks, Multiresolution analysis, remote sensing BibRef

Xu, H.[Han], Le, Z.L.[Zhu-Liang], Huang, J.[Jun], Ma, J.Y.[Jia-Yi],
A Cross-Direction and Progressive Network for Pan-Sharpening,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
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Smadi, A.A.L.[Ahmad A.L.], Yang, S.Y.[Shu-Yuan], Mehmood, A.[Atif], Abugabah, A.[Ahed], Wang, M.[Min], Bashir, M.[Muzaffar],
Smart pansharpening approach using kernel-based image filtering,
IET-IPR(15), No. 11, 2021, pp. 2629-2642.
DOI Link 2108
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Addesso, P.[Paolo], Restaino, R.[Rocco], Vivone, G.[Gemine],
An Improved Version of the Generalized Laplacian Pyramid Algorithm for Pansharpening,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Xie, G.Q.[Guang-Qi], Wang, M.[Mi], Zhang, Z.Q.[Zhi-Qi], Xiang, S.[Shao], He, L.X.[Lu-Xiao],
Near Real-Time Automatic Sub-Pixel Registration of Panchromatic and Multispectral Images for Pan-Sharpening,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhang, Y.H.[Yi-Hang], Atkinson, P.M.[Peter M.], Ling, F.[Feng], Foody, G.M.[Giles M.], Wang, Q.[Qunming], Ge, Y.[Yong], Li, X.D.[Xiao-Dong], Du, Y.[Yun],
Object-Based Area-to-Point Regression Kriging for Pansharpening,
GeoRS(59), No. 10, October 2021, pp. 8599-8614.
IEEE DOI 2109
Satellites, Sensors, Image segmentation, Image sensors, Spatial resolution, Optical sensors, Bandwidth, Downscaling, segmentation BibRef

Huang, W.W.[Wei-Wei], Zhang, Y.[Yan], Zhang, J.W.[Jian-Wei], Zheng, Y.H.[Yu-Hui],
Convolutional Neural Network for Pansharpening with Spatial Structure Enhancement Operator,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Zhang, L.[Libao], Zhang, J.[Jue], Ma, J.[Jie], Jia, X.P.[Xiu-Ping],
SC-PNN: Saliency Cascade Convolutional Neural Network for Pansharpening,
GeoRS(59), No. 11, November 2021, pp. 9697-9715.
IEEE DOI 2111
Pansharpening, Remote sensing, Spatial resolution, Image restoration, Convolution, Task analysis, Proposals, saliency analysis BibRef

Gastineau, A.[Anaïs], Aujol, J.F.[Jean-François], Berthoumieu, Y.[Yannick], Germain, C.[Christian],
Generative Adversarial Network for Pansharpening With Spectral and Spatial Discriminators,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Spatial resolution, Pansharpening, Generative adversarial networks, Vegetation mapping, Satellites, remote sensing BibRef

Tian, X.[Xin], Chen, Y.R.[Yue-Rong], Yang, C.C.[Chang-Cai], Ma, J.Y.[Jia-Yi],
Variational Pansharpening by Exploiting Cartoon-Texture Similarities,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI 2112
Pansharpening, Spatial resolution, Remote sensing, Optimization, Vegetation mapping, TV, Satellites, total variation (TV) BibRef

Ma, W.P.[Wen-Ping], Shen, J.C.[Jian-Chao], Zhu, H.[Hao], Zhang, J.[Jun], Zhao, J.L.[Ji-Liang], Hou, B.[Biao], Jiao, L.C.[Li-Cheng],
A Novel Adaptive Hybrid Fusion Network for Multiresolution Remote Sensing Images Classification,
GeoRS(60), 2022, pp. 1-17.
IEEE DOI 2112
Feature extraction, Spatial resolution, Pansharpening, Data mining, Remote sensing, Fuses, Data integration, Data difference reduction, remote sensing BibRef

Lu, H.Y.[Hang-Yuan], Yang, Y.[Yong], Huang, S.Y.[Shu-Ying], Tu, W.[Wei], Wan, W.G.[Wei-Guo],
A Unified Pansharpening Model Based on Band-Adaptive Gradient and Detail Correction,
IP(31), 2022, pp. 918-933.
IEEE DOI 2201
Pansharpening, Adaptation models, Wavelet transforms, Distortion, Spatial resolution, Satellites, Optimization, Pansharpening, parameter transfer BibRef

Gogineni, R.[Rajesh], Sangani, D.J.[Dhara J.],
A Two-Stage PAN-Sharpening Algorithm Based on Sparse Representation for Spectral Distortion Reduction,
IJIG(22), No. 1 2022, pp. 2250007.
DOI Link 2202
BibRef

Li, S.[Sijia], Guo, Q.[Qing], Li, A.[An],
Pan-Sharpening Based on CNN+ Pyramid Transformer by Using No-Reference Loss,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Jin, Z.R.[Zi-Rong], Zhuo, Y.W.[Yu-Wei], Zhang, T.J.[Tian-Jing], Jin, X.X.[Xiao-Xu], Jing, S.Q.[Shuai-Qi], Deng, L.J.[Liang-Jian],
Remote Sensing Pansharpening by Full-Depth Feature Fusion,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wu, X.[Xiao], Huang, T.Z.[Ting-Zhu], Deng, L.J.[Liang-Jian], Zhang, T.J.[Tian-Jing],
Dynamic Cross Feature Fusion for Remote Sensing Pansharpening,
ICCV21(14667-14676)
IEEE DOI 2203
Visualization, Art, Convolution, Semantics, Neural networks, Pansharpening, Image and video synthesis, Machine learning architectures and formulations BibRef

Liu, X.[Xuan], Tang, P.[Ping], Jin, X.[Xing], Zhang, Z.[Zheng],
From Regression Based on Dynamic Filter Network to Pansharpening by Pixel-Dependent Spatial-Detail Injection,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Wang, Y.Z.[Ya-Zhen], Liu, G.J.[Guo-Jun], Zhang, R.[Rui], Liu, J.[Junmin],
A Two-Stage Pansharpening Method for the Fusion of Remote-Sensing Images,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Wu, Y.Y.[Yuan-Yuan], Feng, S.L.[Si-Ling], Lin, C.[Cong], Zhou, H.J.[Hai-Jie], Huang, M.X.[Meng-Xing],
A Three Stages Detail Injection Network for Remote Sensing Images Pansharpening,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Zhang, F.[Feng], Zhang, K.[Kai], Sun, J.[Jiande],
Multiscale Spatial-Spectral Interaction Transformer for Pan-Sharpening,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Jin, Q.[Qi], Xu, E.[Erqi], Zhang, X.[Xuqing],
A Fusion Method for Multisource Land Cover Products Based on Superpixels and Statistical Extraction for Enhancing Resolution and Improving Accuracy,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Yin, J.[Junru], Qu, J.T.[Jian-Tao], Chen, Q.Q.[Qi-Qiang], Ju, M.[Ming], Yu, J.[Jun],
Differential Strategy-Based Multi-Level Dense Network for Pansharpening,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Saxena, N.[Nidhi], Saxena, G.[Gaurav], Khare, N.[Neelu], Rahman, M.H.[Md Habibur],
Pansharpening scheme using spatial detail injection-based convolutional neural networks,
IET-IPR(16), No. 9, 2022, pp. 2297-2307.
DOI Link 2206
BibRef

Zhang, E.[Erlei], Fu, Y.H.[Yi-Hao], Wang, J.[Jun], Liu, L.[Lu], Yu, K.[Kai], Peng, J.Y.[Jin-Ye],
MSAC-Net: 3D Multi-Scale Attention Convolutional Network for Multi-Spectral Imagery Pansharpening,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Pan, Y.T.[Yue-Tao], Liu, D.F.[Dan-Feng], Wang, L.G.[Li-Guo], Benediktsson, J.A.[Jón Atli], Xing, S.S.[Shi-Shuai],
A Pan-Sharpening Method with Beta-Divergence Non-Negative Matrix Factorization in Non-Subsampled Shear Transform Domain,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Arienzo, A.[Alberto], Alparone, L.[Luciano], Garzelli, A.[Andrea], Lolli, S.[Simone],
Advantages of Nonlinear Intensity Components for Contrast-Based Multispectral Pansharpening,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Huang, W.[Wei], Ju, M.[Ming], Zhao, Z.[Zhuobing], Wu, Q.G.[Qing-Gang], Tian, E.[Erlin],
Local-Global Based High-Resolution Spatial-Spectral Representation Network for Pansharpening,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Yin, J.[Junru], Qu, J.T.[Jian-Tao], Sun, L.[Le], Huang, W.[Wei], Chen, Q.Q.[Qi-Qiang],
A Local and Nonlocal Feature Interaction Network for Pansharpening,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Nie, Z.[Zihao], Chen, L.H.[Li-Hui], Jeon, S.[Seunggil], Yang, X.M.[Xiao-Min],
Spectral-Spatial Interaction Network for Multispectral Image and Panchromatic Image Fusion,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Su, H.[Haonan], Jin, H.Y.[Hai-Yan], Sun, C.[Ce],
Deep Pansharpening via 3D Spectral Super-Resolution Network and Discrepancy-Based Gradient Transfer,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Li, W.S.[Wei-Sheng], He, M.L.[Mao-Lin], Xiang, M.H.[Ming-Hao],
Double-Stack Aggregation Network Using a Feature-Travel Strategy for Pansharpening,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Liu, X.[Xu], Li, L.L.[Ling-Ling], Liu, F.[Fang], Hou, B.[Biao], Yang, S.Y.[Shu-Yuan], Jiao, L.C.[Li-Cheng],
GAFnet: Group Attention Fusion Network for PAN and MS Image High-Resolution Classification,
Cyber(52), No. 10, October 2022, pp. 10556-10569.
IEEE DOI 2209
Feature extraction, Task analysis, Spatial resolution, Satellites, Indexes, Image fusion, Data mining, Classification, deep learning, group spatial-spectral attention BibRef

Wang, X.H.[Xiang-Hai], Mu, Z.H.[Zhen-Hua], Bai, S.F.[Shi-Fu], Feng, Y.N.[Yi-Ning], Song, R.X.[Ruo-Xi],
MS-Pansharpening Algorithm Based on Dual Constraint Guided Filtering,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Yang, X.F.[Xiao-Fei], Nie, R.[Rencan], Zhang, G.[Gucheng], Chen, L.P.[Lu-Ping], Li, H.[He],
DPAFNet: A Multistage Dense-Parallel Attention Fusion Network for Pansharpening,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Wang, W.Q.[Wen-Qing], Zhou, Z.Q.[Zhi-Qiang], Zhang, X.Q.[Xiao-Qiao], Lv, T.[Tu], Liu, H.[Han], Liang, L.[Lili],
DiTBN: Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing Images,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Feng, Y.T.[Yu-Ting], Jin, X.[Xin], Jiang, Q.[Qian], Wang, Q.L.[Quan-Li], Liu, L.[Lin], Yao, S.W.[Shao-Wen],
MPFINet: A Multilevel Parallel Feature Injection Network for Panchromatic and Multispectral Image Fusion,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Zhou, M.[Man], Yan, K.Y.[Ke-Yu], Pan, J.S.[Jin-Shan], Ren, W.Q.[Wen-Qi], Xie, Q.[Qi], Cao, X.Y.[Xiang-Yong],
Memory-Augmented Deep Unfolding Network for Guided Image Super-resolution,
IJCV(131), No. 1, January 2023, pp. 215-242.
Springer DOI 2301
BibRef

Yang, G.[Gang], Zhou, M.[Man], Yan, K.Y.[Ke-Yu], Liu, A.P.[Ai-Ping], Fu, X.Y.[Xue-Yang], Wang, F.[Fan],
Memory-augmented Deep Conditional Unfolding Network for Pansharpening,
CVPR22(1778-1787)
IEEE DOI 2210
Learning systems, Degradation, Deep learning, Neural networks, Pansharpening, Search problems, Photogrammetry and remote sensing BibRef

Shen, K.Q.[Kang-Qing], Yang, X.Y.[Xiao-Yuan], Lolli, S.[Simone], Vivone, G.[Gemine],
A continual learning-guided training framework for pansharpening,
PandRS(196), 2023, pp. 45-57.
Elsevier DOI 2302
Continual learning, Convolutional neural networks, Deep learning, Pansharpening, Image fusion, Remote sensing BibRef

Wu, J.[Jingan], Lin, L.P.[Liu-Peng], Zhang, C.[Chi], Li, T.[Tongwen], Cheng, X.[Xiao], Nan, F.[Fang],
Generating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network,
PandRS(196), 2023, pp. 16-31.
Elsevier DOI 2302
Sentinel-2, Image sharpening, Image fusion, Convolutional neural network BibRef

Sangani, D.J.[Dhara J.], Thakker, R.A.[Rajesh A.], Panchal, S.D., Gogineni, R.[Rajesh],
Pan-Sharpening for Spectral Details Preservation Via Convolutional Sparse Coding in Non-Subsampled Shearlet Space,
IJIG(23), No. 2 2023, pp. 2350013.
DOI Link 2303
BibRef

Jian, L.H.[Li-Hua], Wu, S.[Shaowu], Chen, L.H.[Li-Hui], Vivone, G.[Gemine], Rayhana, R.[Rakiba], Zhang, D.[Di],
Multi-Scale and Multi-Stream Fusion Network for Pansharpening,
RS(15), No. 6, 2023, pp. 1666.
DOI Link 2304
BibRef

Wei, X.X.[Xing-Xing], Yuan, M.[Maoxun],
Adversarial pan-sharpening attacks for object detection in remote sensing,
PR(139), 2023, pp. 109466.
Elsevier DOI 2304
Adversarial pan-sharpening, Remote sensing, Object detection BibRef

Lu, H.Y.[Hang-Yuan], Yang, Y.[Yong], Huang, S.Y.[Shu-Ying], Chen, X.L.[Xiao-Long], Su, H.F.[Hong-Fu], Tu, W.[Wei],
Intensity mixture and band-adaptive detail fusion for pansharpening,
PR(139), 2023, pp. 109434.
Elsevier DOI 2304
Pansharpening, Intensity mixture, Band-adaptive detail fusion, Point spread function BibRef

Liu, P.F.[Peng-Fei],
Pansharpening With Spatial Hessian Non-Convex Sparse and Spectral Gradient Low Rank Priors,
IP(32), 2023, pp. 2120-2131.
IEEE DOI 2304
Pansharpening, Laplace equations, Degradation, Spatial resolution, Satellites, Image fusion, Analytical models, Pansharpening, spectral gradient low rank BibRef

Zhang, X.F.[Xue-Feng], Dai, X.B.[Xia-Bing], Zhang, X.M.[Xue-Min], Hu, Y.C.[Yu-Chen], Kang, Y.D.[Ying-Dong], Jin, G.[Guang],
Improved Generalized IHS Based on Total Variation for Pansharpening,
RS(15), No. 11, 2023, pp. 2945.
DOI Link 2306
BibRef

Wang, T.T.[Ting-Ting], Fang, F.M.[Fa-Ming], Zheng, H.[Hao], Zhang, G.X.[Gui-Xu],
FrMLNet: Framelet-Based Multilevel Network for Pansharpening,
Cyber(53), No. 7, July 2023, pp. 4594-4605.
IEEE DOI 2307
Pansharpening, Spatial resolution, Task analysis, Image reconstruction, Feature extraction, Sensors, spectral and spatial preservation BibRef

He, J.[Jiang], Yuan, Q.Q.[Qiang-Qiang], Li, J.[Jie], Xiao, Y.[Yi], Zhang, L.P.[Liang-Pei],
A self-supervised remote sensing image fusion framework with dual-stage self-learning and spectral super-resolution injection,
PandRS(204), 2023, pp. 131-144.
Elsevier DOI 2310
Pan-sharpening, Unsupervised fusion, Spectral super-resolution, Multispectral images, Self-learning, Remote sensing BibRef

Tao, J.Z.[Jing-Zhe], Ni, W.H.[Wei-Han], Song, C.M.[Chuan-Ming], Wang, X.H.[Xiang-Hai],
FSSBP: Fast Spatial-Spectral Back Projection Based on Pan-Sharpening Iterative Optimization,
RS(15), No. 18, 2023, pp. 4543.
DOI Link 2310
BibRef

Zhang, H.[Hao], Ma, J.Y.[Jia-Yi],
STP-SOM: Scale-Transfer Learning for Pansharpening via Estimating Spectral Observation Model,
IJCV(131), No. 12, December 2023, pp. 3226-3251.
Springer DOI 2311
BibRef

Wang, J.[Jing], Miao, J.Q.[Jia-Qing], Li, G.[Gaoping], Tan, Y.[Ying], Yu, S.C.[Shi-Cheng], Liu, X.G.[Xiao-Guang], Zeng, L.[Li], Li, G.[Guibing],
Pan-Sharpening Network of Multi-Spectral Remote Sensing Images Using Two-Stream Attention Feature Extractor and Multi-Detail Injection (TAMINet),
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Huang, B.[Bo], Li, X.F.[Xiong-Fei], Zhang, X.L.[Xiao-Li],
Triple-loss driven generative adversarial network for pansharpening,
IET-IPR(18), No. 1, 2024, pp. 211-232.
DOI Link 2401
image fusion, neural nets, remote sensing BibRef

Zhang, Z.Q.[Zhi-Qi], Xu, J.[Jun], Wang, X.H.[Xin-Hui], Xie, G.Q.[Guang-Qi], Wei, L.[Lu],
Multiscale Fusion of Panchromatic and Multispectral Images Based on Adaptive Iterative Filtering,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Yang, G.[Gang], Cao, X.Y.[Xiang-Yong], Xiao, W.Z.[Wen-Zhe], Zhou, M.[Man], Liu, A.P.[Ai-Ping], Chen, X.[Xun], Meng, D.Y.[De-Yu],
PanFlowNet: A Flow-Based Deep Network for Pan-sharpening,
ICCV23(16811-16821)
IEEE DOI 2401
BibRef

Li, H.[Hui], Jing, L.H.[Lin-Hai], Dou, C.Y.[Chang-Yong], Ding, H.F.[Hai-Feng],
A Comprehensive Assessment of the Pansharpening of the Nighttime Light Imagery of the Glimmer Imager of the Sustainable Development Science Satellite 1,
RS(16), No. 2, 2024, pp. 245.
DOI Link 2402
BibRef

Shi, K.[Keli], Liu, Z.Q.[Zhi-Qiang], Zhang, W.X.[Wei-Xiong], Tang, P.[Ping], Zhang, Z.[Zheng],
Enhancing Satellite Image Sequences through Multi-Scale Optical Flow-Intermediate Feature Joint Network,
RS(16), No. 2, 2024, pp. 426.
DOI Link 2402
BibRef

Yu, D.[Dian], Zhang, W.[Wei], Xu, M.Z.[Ming-Zhu], Tian, X.[Xin], Jiang, H.[Hao],
Unified Interpretable Deep Network for Joint Super-Resolution and Pansharpening,
RS(16), No. 3, 2024, pp. 540.
DOI Link 2402
BibRef

Gao, Y.[Yi], Qin, M.J.[Meng-Jiao], Wu, S.[Sensen], Zhang, F.[Feng], Du, Z.H.[Zhen-Hong],
GSA-SiamNet: A Siamese Network with Gradient-Based Spatial Attention for Pan-Sharpening of Multi-Spectral Images,
RS(16), No. 4, 2024, pp. 616.
DOI Link 2402
BibRef

Wang, J.M.[Jia-Ming], Zhou, Q.[Qiang], Huang, X.[Xiao], Zhang, R.Q.[Rui-Qian], Chen, X.T.[Xi-Tong], Lu, T.[Tao],
Pan-sharpening via intrinsic decomposition knowledge distillation,
PR(149), 2024, pp. 110247.
Elsevier DOI 2403
Knowledge distillation, Pan-sharpening, Intrinsic decomposition, Image fusion BibRef

Modak, S.[Sourav], Heil, J.[Jonathan], Stein, A.[Anthony],
Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network,
RS(16), No. 5, 2024, pp. 874.
DOI Link 2403
BibRef

Zhang, Z.C.[Zhi-Cheng], Ao, Z.[Zurui], Wu, W.[Wei], Wang, Y.D.[Yi-Dan], Xin, Q.C.[Qin-Chuan],
Developing a Multi-Scale Convolutional Neural Network for Spatiotemporal Fusion to Generate MODIS-like Data Using AVHRR and Landsat Images,
RS(16), No. 6, 2024, pp. 1086.
DOI Link 2403
BibRef

Cheng, Q.[Qing], Xie, R.X.[Rui-Xiang], Wu, J.[Jingan], Ye, F.[Fan],
Deep Learning-Based Spatiotemporal Fusion Architecture of Landsat 8 and Sentinel-2 Data for 10 m Series Imagery,
RS(16), No. 6, 2024, pp. 1033.
DOI Link 2403
BibRef

Han, Y.F.[Yi-Fei], Chi, H.[Hong], Huang, J.L.[Jin-Liang], Gao, X.[Xinyi], Zhang, Z.Y.[Zhi-Yu], Ling, F.[Feng],
TemPanSharpening: A multi-temporal Pansharpening solution based on deep learning and edge extraction,
PandRS(211), 2024, pp. 406-424.
Elsevier DOI 2405
Multi-temporal Pansharpening, Deep learning, Canny edge detector, Residual-in-residual dense block (RRDB), Convolutional block attention module (CBAM) BibRef

Huang, B.[Bo], Li, X.F.[Xiong-Fei], Zhang, X.L.[Xiao-Li],
SWPanGAN: A hybrid generative adversarial network for pansharpening,
IET-IPR(18), No. 8, 2024, pp. 1950-1966.
DOI Link 2406
generative adversarial network, pansharpening, remote sensing images, transformer BibRef

Chen, Y.X.[Ying-Xia], Li, Y.Q.[Yu-Qi], Wang, T.T.[Ting-Ting], Chen, Y.[Yan], Fang, F.[Faming],
DPDU-Net: Double Prior Deep Unrolling Network for Pansharpening,
RS(16), No. 12, 2024, pp. 2141.
DOI Link 2406
BibRef

Jin, X.[Xin], Feng, Y.T.[Yu-Ting], Jiang, Q.[Qian], Miao, S.F.[Sheng-Fa], Chu, X.[Xing], Zheng, H.Q.[Huang-Qimei], Wang, Q.Q.[Qian-Qian],
UPGAN: An Unsupervised Generative Adversarial Network Based on U-Shaped Structure for Pansharpening,
IJGI(13), No. 7, 2024, pp. 222.
DOI Link 2408
BibRef

Wang, S.Y.[Shi-Ying], Zou, X.C.[Xue-Chao], Li, K.[Kai], Xing, J.L.[Jun-Liang], Cao, T.F.[Teng-Fei], Tao, P.[Pin],
Towards Robust Pansharpening: A Large-Scale High-Resolution Multi-Scene Dataset and Novel Approach,
RS(16), No. 16, 2024, pp. 2899.
DOI Link 2408
BibRef

Liu, D.F.[Dan-Feng], Wang, E.[Enyuan], Wang, L.G.[Li-Guo], Benediktsson, J.A.[Jón Atli], Wang, J.Y.[Jian-Yu], Deng, L.[Lei],
Pansharpening Based on Multimodal Texture Correction and Adaptive Edge Detail Fusion,
RS(16), No. 16, 2024, pp. 2941.
DOI Link 2408
BibRef

Ciotola, M.[Matteo], Guarino, G.[Giuseppe], Scarpa, G.[Giuseppe],
An Unsupervised CNN-Based Pansharpening Framework with Spectral-Spatial Fidelity Balance,
RS(16), No. 16, 2024, pp. 3014.
DOI Link 2408
BibRef

Ke, C.J.[Cheng-Jie], Zhang, Z.Y.[Zhi-Yuan], Zhang, W.[Wei], Chen, J.[Jun], Tian, X.[Xin],
Kernel-guided injection deep network for blind fusion of multispectral and panchromatic images,
PR(157), 2025, pp. 110889.
Elsevier DOI 2409
Image fusion, Blind fusion, Detail injection, Deep learning BibRef

Xing, Y.H.[Ying-Hui], Qu, L.T.[Li-Tao], Zhang, S.Z.[Shi-Zhou], Zhang, K.[Kai], Zhang, Y.N.[Yan-Ning], Bruzzone, L.[Lorenzo],
CrossDiff: Exploring Self-Supervised Representation of Pansharpening via Cross-Predictive Diffusion Model,
IP(33), 2024, pp. 5496-5509.
IEEE DOI Code:
WWW Link. 2410
Pansharpening, Diffusion models, Feature extraction, Spatial resolution, Training, Noise reduction, Transformers, denoising diffusion probabilistic model BibRef

Tagami, R.[Rina], Kobayashi, H.[Hiroki], Akizuki, S.[Shuichi], Hashimoto, M.[Manabu],
Reliable Image Matching Using Optimal Combination of Color and Intensity Information Based on Relationship with Surrounding Objects,
IEICE(E108-D), No. 10, October 2024, pp. 1312-1321.
WWW Link. 2410
BibRef

Zhang, Z.H.[Zhen-Hua], Zhang, S.[Shenfu], Meng, X.C.[Xiang-Chao], Chen, L.[Liang], Shao, F.[Feng],
Perceptual Quality Assessment for Pansharpened Images Based on Deep Feature Similarity Measure,
RS(16), No. 24, 2024, pp. 4621.
DOI Link 2501
BibRef

Cui, Y.C.[Yong-Chuan], Liu, P.[Peng], Ma, Y.[Yan], Chen, L.[Lajiao], Xu, M.Z.[Meng-Zhen], Guo, X.Y.[Xing-Yan],
Pansharpening via predictive filtering with element-wise feature mixing,
PandRS(219), 2025, pp. 22-37.
Elsevier DOI Code:
WWW Link. 2501
Pansharpening, Image fusion, Predictive filtering, Deep learning BibRef

Restaino, R.[Rocco],
Pansharpening Techniques: Optimizing the Loss Function for Convolutional Neural Networks,
RS(17), No. 1, 2025, pp. 16.
DOI Link 2501
BibRef

Wen, X.[Xincan], Ma, H.B.[Hong-Bing], Li, L.L.[Liang-Liang],
A Three-Branch Pansharpening Network Based on Spatial and Frequency Domain Interaction,
RS(17), No. 1, 2025, pp. 13.
DOI Link 2501
BibRef

Zhang, J.[Jie], Cao, K.[Ke], Yan, K.Y.[Ke-Yu], Lin, Y.L.[Yun-Long], He, X.[Xuanhua], Wang, Y.Y.[Ying-Ying], Li, R.[Rui], Xie, C.J.[Cheng-Jun], Zhang, J.[Jun], Zhou, M.[Man],
Frequency Decoupled Domain-Irrelevant Feature Learning for Pan-Sharpening,
CirSysVideo(35), No. 2, February 2025, pp. 1237-1250.
IEEE DOI 2502
Representation learning, Frequency-domain analysis, Feature extraction, Data mining, Training, Spatial resolution, amplitude spectrums BibRef

Sangani, D.J.[Dhara J.], Thakker, R.A.[Rajesh A.], Panchal, S.D., Gogineni, R.[Rajesh],
Remote Sensing Pansharpening with TV-H-1 Decomposition and PSO-Based Adaptive Weighting Method,
IJIG(25), No. 1, Januaury 2025, pp. 2450061.
DOI Link 2502
BibRef

Ebrahimy, H.[Hamid], Yu, T.[Tong], Zhang, Z.[Zhou],
Developing a spatiotemporal fusion framework for generating daily UAV images in agricultural areas using publicly available satellite data,
PandRS(220), 2025, pp. 413-427.
Elsevier DOI 2502
Agricultural monitoring, Time series, Harmonized Landsat and Sentinel-2, Unmanned aerial vehicle, Temporal resolution BibRef

Wang, Q.M.[Qun-Ming], Ma, W.J.[Wen-Jing], Liu, S.[Sicong], Tong, X.H.[Xiao-Hua], Atkinson, P.M.[Peter M.],
Data fidelity-oriented spatial-spectral fusion of CRISM and CTX images,
PandRS(220), 2025, pp. 172-191.
Elsevier DOI 2502
Compact Reconnaissance Imaging Spectrometer for Mars (CRISM), Context Camera (CTX), Downscaling, Spatial-spectral fusion, Area-to-point kriging (ATPK) BibRef

Armannsson, S.E.[Sveinn E.], Ulfarsson, M.O.[Magnus O.], Sigurdsson, J.[Jakob],
A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery,
RS(17), No. 3, 2025, pp. 432.
DOI Link 2502
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Lu, H.Y.[Hang-Yuan], Yang, Y.[Yong], Huang, S.Y.[Shu-Ying], Liu, R.[Rixian], Guo, H.M.[Hui-Min],
MSAN: Multiscale self-attention network for pansharpening,
PR(162), 2025, pp. 111441.
Elsevier DOI 2503
Pansharpening, Multiscale, Self-attention, Swin Transformer BibRef

Wang, M.X.[Mei-Xiang], Zhang, W.J.[Wen-Juan], Wang, B.[Bowen], Ma, X.S.[Xue-Song], Qi, P.[Peng], Zhou, Z.X.[Zi-Xiang],
The Improved MNSPI Method for MODIS Surface Reflectance Data Small-Area Restoration,
RS(17), No. 6, 2025, pp. 1022.
DOI Link 2503
Large scale use of lower resolution data. BibRef

Liu, P.F.[Peng-Fei], Liu, J.H.[Jia-Hui],
D2PNet: Deep Detail Priors for Multispectral Image Fusion,
SPLetters(32), 2025, pp. 2778-2782.
IEEE DOI 2508
Pansharpening, TV, Tensors, Image fusion, Visualization, Symbols, Spatial resolution, Modulation, Fuses, Convolution, PnP BibRef

Miao, R.[Rui], Peng, F.G.[Feng-Guang], Dong, D.[Dong], Shi, H.[Hang],
Progressive frequency-decoupled network with neighbor-aware self-evolving convolution kernels for pansharpening,
PR(169), 2026, pp. 111888.
Elsevier DOI 2509
Pansharpening, Convolution kernel, Image fusion, Progressive network BibRef

Wang, J.M.[Jia-Ming], Chen, X.T.[Xi-Tong], Huang, X.[Xiao], Zhang, R.Q.[Rui-Qian], Wang, Y.[Yu], Lu, T.[Tao],
Rethinking the Role of Panchromatic Images in Pan-Sharpening,
MultMed(27), 2025, pp. 4558-4570.
IEEE DOI 2509
Mutual information, Spatial resolution, Satellites, Feature extraction, Data mining, Contrastive learning, Training, pan-sharpening BibRef

Zhang, J.[Junkang], Fang, F.[Faming], Wang, T.T.[Ting-Ting], Zhang, G.X.[Gui-Xu], Song, H.[Haichuan],
FrDiff: Framelet-Based Conditional Diffusion Model for Multispectral and Panchromatic Image Fusion,
MultMed(27), 2025, pp. 5989-6002.
IEEE DOI 2510
Pansharpening, Diffusion models, Spatial resolution, Remote sensing, Noise reduction, Multiresolution analysis, framelet transform BibRef

Huang, J.[Jie], Chen, H.[Haorui], Ren, J.X.[Jia-Xuan], Peng, S.[Siran], Deng, L.J.[Liang-Jian],
A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening,
CVPR25(7447-7456)
IEEE DOI Code:
WWW Link. 2508
Adaptation models, Visualization, Correlation, Redundancy, Buildings, Refining, Pansharpening, Covariance matrices, Remote sensing, covariance BibRef

Xing, Y.H.[Ying-Hui], Qu, L.T.[Li-Tao], Zhang, S.Z.[Shi-Zhou], Xu, D.[Di], Yang, Y.K.[Ying-Kun], Zhang, Y.N.[Yan-Ning],
Dual-Granularity Semantic Guided Sparse Routing Diffusion Model for General Pansharpening,
CVPR25(12658-12668)
IEEE DOI Code:
WWW Link. 2508
Training, Adaptation models, Satellites, Semantics, Pansharpening, Routing, Diffusion models, Sensors, Rivers BibRef

Xiao, J.L.[Jin-Liang], Huang, T.Z.[Ting-Zhu], Deng, L.J.[Liang-Jian], Lin, G.[Guang], Cao, Z.[Zihan], Li, C.[Chao], Zhao, Q.[Qibin],
Hyperspectral Pansharpening via Diffusion Models with Iteratively Zero-Shot Guidance,
CVPR25(12669-12678)
IEEE DOI Code:
WWW Link. 2508
Training, Measurement, Knowledge engineering, Visualization, Neural networks, Pansharpening, Network architecture, Hyperspectral imaging BibRef

Kim, S.[Sungpyo], Do, J.[Jeonghyeok], Lee, J.[Jaehyup], Kim, M.C.[Mun-Churl],
U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening,
CVPR25(23069-23079)
IEEE DOI Code:
WWW Link. 2508
Wavelet transforms, Uncertainty, Computational modeling, Vectors, Robustness, Image restoration, Satellite images, Decoding BibRef

Zhao, M.J.[Meng-Jiao], Ma, M.T.[Meng-Ting], Gao, A.[Ao], Zhang, W.[Wei],
Frequency-Spatial Domain Information Fusion Network for Pan-Sharpening,
ICIP24(1718-1724)
IEEE DOI 2411
Learning systems, Frequency modulation, Satellites, Frequency-domain analysis, Neural networks, remote sensing BibRef

Wang, X.Y.[Xue-Yang], Zheng, Z.X.[Zhi-Xin], Shao, J.D.[Jian-Dong], Duan, Y.[Yule], Deng, L.J.[Liang-Jian],
Adaptive Rectangular Convolution for Remote Sensing Pansharpening,
CVPR25(17872-17881)
IEEE DOI Code:
WWW Link. 2508
Convolutional codes, Visualization, Convolution, Shape, Source coding, Pansharpening, Network architecture, Kernel, arnet BibRef

Duan, Y.[Yule], Wu, X.[Xiao], Deng, H.Y.[Hao-Yu], Deng, L.J.[Liang-Jian],
Content-Adaptive Non-Local Convolution for Remote Sensing Pansharpening,
CVPR24(27738-27747)
IEEE DOI Code:
WWW Link. 2410
Learning systems, Visualization, Limiting, Convolution, Source coding, Pansharpening, Network architecture BibRef

Zhou, M.[Man], Huang, J.[Jie], Zheng, N.[Naishan], Li, C.Y.[Chong-Yi],
Learned Image Reasoning Prior Penetrates Deep Unfolding Network for Panchromatic and Multi-Spectral Image Fusion,
ICCV23(12364-12373)
IEEE DOI Code:
WWW Link. 2401
BibRef

Zhu, Z.[Zeyu], Cao, X.Y.[Xiang-Yong], Zhou, M.[Man], Huang, J.H.[Jun-Hao], Meng, D.Y.[De-Yu],
Probability-based Global Cross-modal Upsampling for Pansharpening,
CVPR23(14039-14048)
IEEE DOI 2309
BibRef

Li, Y.[Yan], Li, J.M.[Jian-Min], Du, X.F.[Xiao-Feng], Huang, Y.[Yibo], Lei, J.[Jian],
An Improved Method for Pan-Sharpening Based on Pan-GAN,
ICIVC22(282-286)
IEEE DOI 2301
Training, Visualization, Feature extraction, Generative adversarial networks, Generators, Data mining, Generative adversarial network BibRef

Zhong, S.W.[Sheng-Wei], Zhang, Y.[Ye],
Fusion of Multispectral and Panchromatic Images Based on a Novel Inter-Band Structure Model,
ICIP15(457-461)
IEEE DOI 1512
ARSIS Concept BibRef

Yan, K.Y.[Ke-Yu], Zhou, M.[Man], Zhang, L.[Li], Xie, C.J.[Cheng-Jun],
Memory-Augmented Model-Driven Network for Pansharpening,
ECCV22(XIX:306-322).
Springer DOI 2211
BibRef

Tan, J.T.[Jiang-Tong], Huang, J.[Jie], Zheng, N.[Naishan], Zhou, M.[Man], Yan, K.Y.[Ke-Yu], Hong, D.F.[Dan-Feng], Zhao, F.[Feng],
Revisiting Spatial-Frequency Information Integration from a Hierarchical Perspective for Panchromatic and Multi-Spectral Image Fusion,
CVPR24(25922-25931)
IEEE DOI Code:
WWW Link. 2410
Codes, Fuses, Superresolution, Space exploration, Image fusion, Pan-sharpening BibRef

Zhou, M.[Man], Huang, J.[Jie], Yan, K.Y.[Ke-Yu], Yu, H.[Hu], Fu, X.Y.[Xue-Yang], Liu, A.P.[Ai-Ping], Wei, X.[Xian], Zhao, F.[Feng],
Spatial-Frequency Domain Information Integration for Pan-Sharpening,
ECCV22(XVIII:274-291).
Springer DOI 2211
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Sun, Y.[Yi], Zhang, Y.L.[Yuan-Lin], Yuan, Y.[Yuan],
Adaptive Detail Injection-Based Feature Pyramid Network for Pan-Sharpening,
ICIP22(1646-1650)
IEEE DOI 2211
Adaptation models, Adaptive systems, Codes, Distortion, Image fusion, Pan-sharpening, image fusion, detail injection, feature pyramid, detail perception BibRef

Bandara, W.G.C.[Wele Gedara Chaminda], Patel, V.M.[Vishal M.],
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening,
CVPR22(1757-1767)
IEEE DOI 2210
Codes, Fuses, Pansharpening, Feature extraction, Transformers, Extraterrestrial measurements, Photogrammetry and remote sensing BibRef

Zhou, M.[Man], Yan, K.Y.[Ke-Yu], Huang, J.[Jie], Yang, Z.[Zihe], Fu, X.[Xueyang], Zhao, F.[Feng],
Mutual Information-driven Pan-sharpening,
CVPR22(1788-1798)
IEEE DOI 2210
Satellites, Redundancy, Pansharpening, Minimization, Mutual information, Photogrammetry and remote sensing BibRef

Gao, J.H.[Jian-Hao], Li, J.[Jie], Su, X.[Xin], Jiang, M.H.[Meng-Hui], Yuan, Q.Q.[Qiang-Qiang],
Deep Image Interpolation: A Unified Unsupervised Framework for Pansharpening,
NTIRE22(608-617)
IEEE DOI 2210
Training, Deep learning, Interpolation, Satellites, Neural networks, Data integration, Pansharpening BibRef

Wang, Y.J.[Ya-Jie], Xie, Y.Y.[Yan-Yan], Wu, Y.Y.[Yan-Yan], Liang, K.[Kai], Qiao, J.L.[Ji-Lin],
An Unsupervised Multi-scale Generative Adversarial Network for Remote Sensing Image Pan-Sharpening,
MMMod22(II:356-368).
Springer DOI 2203
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Wang, J.M.[Jia-Ming], Shao, Z.F.[Zhen-Feng], Huang, X.[Xiao], Lu, T.[Tao], Zhang, R.Q.[Rui-Qian], Ma, J.Y.[Jia-Yi],
Pan-Sharpening Via High-Pass Modification Convolutional Neural Network,
ICIP21(1714-1718)
IEEE DOI 2201
Image edge detection, Neural networks, Distortion, Image restoration, Convolutional neural networks, deep neural networks BibRef

Xu, S.[Shuang], Zhang, J.S.[Jiang-She], Zhao, Z.X.[Zi-Xiang], Sun, K.[Kai], Liu, J.[Junmin], Zhang, C.X.[Chun-Xia],
Deep Gradient Projection Networks for Pan-sharpening,
CVPR21(1366-1375)
IEEE DOI 2111
Image resolution, Satellites, Stacking, Neural networks, Tools, Iterative algorithms BibRef

Lee, J.[Jaehyup], Seo, S.[Soomin], Kim, M.C.[Mun-Churl],
SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery,
CVPR21(10161-10169)
IEEE DOI 2111
Visualization, Satellites, Image edge detection, Merging, Feature extraction, Probabilistic logic BibRef

Fang, S., Wang, X., Zhang, J., Cao, Y.,
Pan-Sharpening Based On Parallel Pyramid Convolutional Neural Network,
ICIP20(453-457)
IEEE DOI 2011
Spatial resolution, Satellites, Feature extraction, Remote sensing, Correlation, Distortion, parallel pyramid network, pan-sharpening, learning-based BibRef

Bello, J.L.G., Seo, S., Kim, M.,
Pan-Sharpening With Color-Aware Perceptual Loss And Guided Re-Colorization,
ICIP20(908-912)
IEEE DOI 2011
Image color analysis, Task analysis, Spatial resolution, Satellites, Training, Network architecture, Pan-sharpening, satellite imagery. BibRef

Rui, X., Cao, Y., Kang, Y., Song, W., Ba, R.,
Maskpan: Mask Prior Guided Network For Pansharpening,
ICIP20(853-857)
IEEE DOI 2011
Indexes, Visualization, Economic indicators, pansharpening, mask prior, feature fusion, attention mechanism, semantic segmentation BibRef

Gastineau, A., Aujol, J.F., Berthoumieu, Y., Germain, C.,
A Residual Dense Generative Adversarial Network For Pansharpening With Geometrical Constraints,
ICIP20(493-497)
IEEE DOI 2011
Generators, Spatial resolution, Generative adversarial networks, Geometry, Satellites, remote sensing BibRef

Zhang, L.B.[Li-Bao], Zhu, W.N.[Wan-Ning], Sun, Y.[Yang],
Pan-Sharpening Based On Joint Saliency Detection For Multiple Remote Sensing Images,
ICIP20(1093-1097)
IEEE DOI 2011
Sun, Indexes, Hafnium, Remote sensing image, pan-sharpening, joint saliency analysis, co-clustering, compensation strategy BibRef

Fu, X.Y.[Xue-Yang], Lin, Z.H.[Zi-Huang], Huang, Y.[Yue], Ding, X.H.[Xing-Hao],
A Variational Pan-Sharpening With Local Gradient Constraints,
CVPR19(10257-10266).
IEEE DOI 2002
BibRef

Dadras Javan, F., Mortazavi, F.S., Moradi, F., Toosi, A.,
New Hybrid Pan-sharpening Method Based On Type-1 Fuzzy-DWT Strategy,
SMPR19(247-254).
DOI Link 1912
BibRef

Yoo, J., Kim, J.,
Enhancing Denoised Image Via Fusion With a Noisy Image,
ICIP19(1790-1794)
IEEE DOI 1910
Image denoising, texture, PCA, sparsity, cost optimization BibRef

Zhang, L., Zhang, J., Lyu, X., Ma, J.,
A New Pansharpening Method Using Objectness Based Saliency Analysis and Saliency Guided Deep Residual Network,
ICIP19(4529-4533)
IEEE DOI 1910
Image fusion, pansharpening, deep residual network, saliency, normalized mean square error BibRef

Zhou, L., Luo, X., Yin, J., Shi, X.,
Spectral Diversity Enhancement for Pansharpening,
ICIP18(1867-1871)
IEEE DOI 1809
Interpolation, Indexes, Spatial resolution, Dictionaries, Remote sensing, Distortion, Optimization, pansharpening, sparse representation BibRef