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deconvolution
BibRef
Wang, Q.M.[Qun-Ming],
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IEEE DOI
1609
feature extraction
See also Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class.
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Wang, Q.M.[Qun-Ming],
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Sub-pixel Target Mapping from Soft-classified, Remotely Sensed Imagery,
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Downscaling Cokriging for Super-Resolution Mapping of Continua in
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IEEE DOI
0801
BibRef
Nguyen, M.Q.,
Atkinson, P.M.,
Lewis, H.G.,
Superresolution Mapping Using a Hopfield Neural Network With Fused
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GeoRS(44), No. 3, March 2006, pp. 736-749.
IEEE DOI
0604
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Fryer, J.G.[John G.],
McIntosh, K.[Kerry],
Enhancement of Image Resolution in Digital Photogrammetry,
PhEngRS(67), No. 6, June 2001, pp. 741-750.
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0108
Rigorously combine multiple low-resolution digital
images into a higher resolution composite, apply to photogrammetric
DEM generation.
BibRef
Galbraith, A.E.,
Theiler, J.,
Thome, K.J.,
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Resolution Enhancement of Multilook Imagery for the Multispectral
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IEEE DOI
0509
BibRef
Long, D.G.,
Spencer, M.W.,
Njoku, E.G.,
Spatial Resolution and Processing Tradeoffs for HYDROS:
Application of Reconstruction and Resolution Enhancement Techniques,
GeoRS(43), No. 1, January 2005, pp. 3-12.
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Akgun, T.,
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IEEE DOI
0510
BibRef
Merino, M.T.[Maria Teresa],
Nunez, J.[Jorge],
Super-Resolution of Remotely Sensed Images With Variable-Pixel Linear
Reconstruction,
GeoRS(45), No. 5, May 2007, pp. 1446-1457.
IEEE DOI
0704
BibRef
Boucher, A.[Alexandre],
Kyriakidis, P.C.[Phaedon C.],
Integrating Fine Scale Information in Super-resolution Land-cover
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PhEngRS(73), No. 8, August 2007, pp. 913-922.
WWW Link.
0709
Accounting for additional fine spatial resolution information can lead
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BibRef
Boucher, A.[Alexandre],
Kyriakidis, P.C.[Phaedon C.],
Cronkite-Ratcliff, C.,
Geostatistical Solutions for Super-Resolution Land Cover Mapping,
GeoRS(46), No. 1, January 2008, pp. 272-283.
IEEE DOI
0712
BibRef
Zhang, H.Y.[Hong-Yan],
Yang, Z.Y.[Ze-Yu],
Zhang, L.P.[Liang-Pei],
Shen, H.F.[Huan-Feng],
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DOI Link
1402
BibRef
Haris, M.,
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Fan, L.,
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Nobuhara, H.,
Superresolution for UAV Images via Adaptive Multiple Sparse
Representation and Its Application to 3-D Reconstruction,
GeoRS(55), No. 7, July 2017, pp. 4047-4058.
IEEE DOI
1706
Dictionaries, Image edge detection, Image resolution, Imaging,
Monitoring, Training, Unmanned aerial vehicles, 3-D images,
aerial image, agriculture, monitoring, phenotyping,
sparse representation, superresolution (SR), unmanned, aerial,
vehicle, (UAV)
BibRef
Zhang, T.[Ting],
Du, Y.[Yi],
Lu, F.F.[Fang-Fang],
Super-Resolution Reconstruction of Remote Sensing Images Using
Multiple-Point Statistics and Isometric Mapping,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link
1708
BibRef
Chang, Y.P.[Yun-Peng],
Luo, B.[Bin],
Bidirectional Convolutional LSTM Neural Network for Remote Sensing
Image Super-Resolution,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link
1910
BibRef
Dong, X.Y.[Xiao-Yu],
Xi, Z.H.[Zhi-Hong],
Sun, X.[Xu],
Gao, L.R.[Lian-Ru],
Transferred Multi-Perception Attention Networks for Remote Sensing
Image Super-Resolution,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Dong, X.Y.[Xiao-Yu],
Wang, L.G.[Long-Guang],
Sun, X.[Xu],
Jia, X.P.[Xiu-Ping],
Gao, L.R.[Lian-Ru],
Zhang, B.[Bing],
Remote Sensing Image Super-Resolution Using Second-Order Multi-Scale
Networks,
GeoRS(59), No. 4, April 2021, pp. 3473-3485.
IEEE DOI
2104
Remote sensing, Image reconstruction, Spatial resolution,
Convolution, Feature extraction, Task analysis, Feature reuse,
super-resolution (SR)
BibRef
Dong, X.Y.[Xiao-Yu],
Sun, X.[Xu],
Jia, X.P.[Xiu-Ping],
Xi, Z.H.[Zhi-Hong],
Gao, L.R.[Lian-Ru],
Zhang, B.[Bing],
Remote Sensing Image Super-Resolution Using Novel Dense-Sampling
Networks,
GeoRS(59), No. 2, February 2021, pp. 1618-1633.
IEEE DOI
2101
Image reconstruction, Remote sensing, Feature extraction,
Spatial resolution, Convolutional neural networks,
wide activation
See also Multi-Resolution Weed Classification via Convolutional Neural Network and Superpixel Based Local Binary Pattern Using Remote Sensing Images.
BibRef
Chopade, P.B.[Pravin B.],
Patil, P.M.[Pradeep M.],
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1901
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JOSA-A(35), No. 7, July 2018, pp. 1212-1220.
DOI Link
1912
Image processing, Image reconstruction techniques,
Computational imaging, Image processing, Image quality, Signal processing
BibRef
Gong, Y.F.[Yuan-Fu],
Liao, P.[Puyun],
Zhang, X.D.[Xiao-Dong],
Zhang, L.[Lifei],
Chen, G.Z.[Guan-Zhou],
Zhu, K.[Kun],
Tan, X.L.[Xiao-Liang],
Lv, Z.Y.[Zhi-Yong],
Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution
Remote Sensing Images,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Zhang, D.Y.[Dong-Yang],
Shao, J.[Jie],
Li, X.Y.[Xin-Yao],
Shen, H.T.[Heng Tao],
Remote Sensing Image Super-Resolution via Mixed High-Order Attention
Network,
GeoRS(59), No. 6, June 2021, pp. 5183-5196.
IEEE DOI
2106
Remote sensing, Feature extraction, Image resolution,
Image restoration, Image reconstruction, Task analysis, Satellites,
satellite image
BibRef
Peng, Y.[Yali],
Wang, X.N.[Xu-Ning],
Zhang, J.W.[Jun-Wei],
Liu, S.G.[Shi-Gang],
Pre-training of gated convolution neural network for remote sensing
image super-resolution,
IET-IPR(15), No. 5, 2021, pp. 1179-1188.
DOI Link
2106
BibRef
Zhao, M.H.[Ming-Hua],
Ning, J.W.[Jia-Wei],
Hu, J.[Jing],
Li, T.T.[Ting-Ting],
Hyperspectral Image Super-Resolution under the Guidance of Deep
Gradient Information,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Huan, H.[Hai],
Li, P.C.[Peng-Cheng],
Zou, N.[Nan],
Wang, C.[Chao],
Xie, Y.Q.[Ya-Qin],
Xie, Y.[Yong],
Xu, D.D.[Dong-Dong],
End-to-End Super-Resolution for Remote-Sensing Images Using an
Improved Multi-Scale Residual Network,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Huang, B.[Bo],
He, B.[Boyong],
Wu, L.[Liaoni],
Guo, Z.M.[Zhi-Ming],
Deep Residual Dual-Attention Network for Super-Resolution
Reconstruction of Remote Sensing Images,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link
2107
BibRef
Tao, Y.T.[Yi-Ting],
Xu, M.Z.[Miao-Zhong],
Zhong, Y.F.[Yan-Fei],
Cheng, Y.F.[Yu-Feng],
GAN-Assisted Two-Stream Neural Network for High-Resolution Remote
Sensing Image Classification,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link
1802
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Hu, F.[Fan],
Xia, G.S.[Gui-Song],
Hu, J.W.[Jing-Wen],
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DOI Link
1512
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Tao, Y.T.[Yi-Ting],
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Du, B.[Bo],
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Unsupervised-Restricted Deconvolutional Neural Network for Very High
Resolution Remote-Sensing Image Classification,
GeoRS(55), No. 12, December 2017, pp. 6805-6823.
IEEE DOI
1712
Use small number of labeled pixels.
Data models, Deconvolution, Feature extraction, Image resolution,
Remote sensing, Satellites, Training,
very high resolution (VHR) image per-pixel classification
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Xu, R.D.[Ru-Dong],
Tao, Y.T.[Yi-Ting],
Lu, Z.Y.[Zhong-Yuan],
Zhong, Y.F.[Yan-Fei],
Attention-Mechanism-Containing Neural Networks for High-Resolution
Remote Sensing Image Classification,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link
1811
BibRef
Ma, Y.C.[Yun-Chuan],
Lv, P.Y.[Peng-Yuan],
Liu, H.[Hao],
Sun, X.H.[Xue-Hong],
Zhong, Y.F.[Yan-Fei],
Remote Sensing Image Super-Resolution Based on Dense Channel
Attention Network,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Yang, X.[Xin],
Xie, T.X.[Tang-Xin],
Guo, Y.Q.[Ying-Qing],
Zhou, D.[Dake],
Remote sensing image super-resolution based on convolutional blind
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IET-IPR(15), No. 11, 2021, pp. 2508-2520.
DOI Link
2108
BibRef
Zhang, L.Z.[Li-Ze],
Lu, W.[Wen],
Huang, Y.F.[Yuan-Fei],
Sun, X.P.[Xiao-Peng],
Zhang, H.Y.[Hong-Yi],
Unpaired Remote Sensing Image Super-Resolution with Multi-Stage
Aggregation Networks,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link
2109
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Choi, Y.[Yeonju],
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A No-Reference CNN-Based Super-Resolution Method for KOMPSAT-3 Using
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DOI Link
2109
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Li, Y.H.[Yin-Hao],
Iwamoto, Y.[Yutaro],
Lin, L.F.[Lan-Fen],
Chen, Y.W.[Yen-Wei],
Parallel-connected Residual Channel Attention Network for Remote
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MLCSA20(18-30).
Springer DOI
2103
BibRef
Shin, C.,
Kim, S.,
Kim, Y.,
From Planetscope To Worldview: Micro-Satellite Image Super-Resolution
With Optimal Transport Distance,
ICIP20(898-902)
IEEE DOI
2011
Degradation, Remote sensing, Satellites, Histograms,
Image resolution, Training, Generators, Micro-satellite image,
degradation learning
BibRef
Zhu, X.,
Talebi, H.,
Shi, X.,
Yang, F.,
Milanfar, P.,
Super-Resolving Commercial Satellite Imagery Using Realistic Training
Data,
ICIP20(498-502)
IEEE DOI
2011
Satellites, Training data, Data models, Kernel, Spatial resolution,
Degradation, Remote sensing, satellite imagery, super-resolution
BibRef
Nair, P.,
Unni, V.S.,
Chaudhury, K.N.,
Hyperspectral Image Fusion Using Fast High-Dimensional Denoising,
ICIP19(3123-3127)
IEEE DOI
1910
hyperspectral image fusion, plug-and-play, regularization,
high-dimensional denoiser
BibRef
Bosch, M.,
Gifford, C.M.,
Rodriguez, P.A.,
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial
Learning,
WACV18(1414-1422)
IEEE DOI
1806
convolution, feedforward neural nets, image resolution,
learning (artificial intelligence), stereo image processing,
Training
BibRef
Hu, W.G.[Wen-Guang],
Hu, T.B.[Ting-Bo],
Wu, T.[Tao],
Zhang, B.[Bo],
Liu, Q.[Qixu],
Sea-surface image super-resolution based on sparse representation,
IASP11(102-107).
IEEE DOI
1112
BibRef
Zomet, A.[Assaf],
Peleg, S.[Shmuel],
Multi-sensor super-resolution,
WACV02(27-31).
IEEE DOI
0303
BibRef
Earlier:
Efficient Super-resolution and Applications to Mosaics,
ICPR00(Vol I: 579-583).
IEEE DOI
0009
BibRef
Earlier:
Applying Super-Resolution to Panoramic Mosaics,
WACV98(286-287).
IEEE DOI
9809
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Super Resolution for Sentinel Sensors .