12.1.5.3 Radar, SAR Image Change Detection

Chapter Contents (Back)
Change Detection. Radar Change Detection. SAR Change Detection.
See also Radar, SAR Registration.
See also Change Detection for Damage Assessment.
See also Land Cover Change Analysis, Remote Sensing Change Analysis, Temporal Analysis.

Dekker, R.J.,
Speckle Filtering in Satellite SAR Change Detection Imagery,
JRS(19), No. 6, April 1998, pp. 1133-1146. 9805
BibRef

Lombardo, P., Oliver, C.J.,
Maximum likelihood approach to the detection of changes between multitemporal SAR images,
IEE-P(RSN: 148), No. 4, 2001, pp. 200-210.
DOI Link 1102
BibRef

Williams, M.L.[Mark L.], Preiss, M.[Mark],
Physics-Based Predictions for Coherent Change Detection Using X-Band Synthetic Aperture Radar,
JASP(2005), No. 20, 2005, pp. 3243-3258.
WWW Link. 0603
BibRef

Bazi, Y., Bruzzone, L., Melgani, F.,
An Unsupervised Approach Based on the Generalized Gaussian Model to Automatic Change Detection in Multitemporal SAR Images,
GeoRS(43), No. 4, April 2005, pp. 874-887.
IEEE Abstract. 0501
BibRef

Pirrone, D., Bovolo, F., Bruzzone, L.,
A Novel Framework Based on Polarimetric Change Vectors for Unsupervised Multiclass Change Detection in Dual-Pol Intensity SAR Images,
GeoRS(58), No. 7, July 2020, pp. 4780-4795.
IEEE DOI 2006
Scattering, Backscatter, Radar polarimetry, Synthetic aperture radar, Sensitivity, Sensors, Surface waves, polarimetric synthetic aperture radar (PolSAR) BibRef

Bazi, Y., Melgani, F.,
Gaussian Process Approach to Remote Sensing Image Classification,
GeoRS(48), No. 1, January 2010, pp. 186-197.
IEEE DOI 1001

See also Semisupervised PSO-SVM Regression for Biophysical Parameter Estimation. BibRef

Narayan, U., Lakshmi, V., Jackson, T.J.,
High-Resolution Change Estimation of Soil Moisture Using L-Band Radiometer and Radar Observations Made During the SMEX02 Experiments,
GeoRS(44), No. 6, June 2006, pp. 1545-1554.
IEEE DOI 0606
BibRef

Ranney, K.I., Soumekh, M.,
Signal Subspace Change Detection in Averaged Multilook SAR Imagery,
GeoRS(44), No. 1, January 2006, pp. 201-213.
IEEE DOI 0601
BibRef

Carincotte, C., Derrode, S., Bourennane, S.,
Unsupervised Change Detection on SAR Images Using Fuzzy Hidden Markov Chains,
GeoRS(44), No. 2, February 2006, pp. 432-441.
IEEE DOI 0602
BibRef

Moser, G., Serpico, S.B.,
Generalized Minimum-Error Thresholding for Unsupervised Change Detection From SAR Amplitude Imagery,
GeoRS(44), No. 10, October 2006, pp. 2972-2982.
IEEE DOI 0609
\ BibRef

Moser, G.[Gabriele], Serpico, S.B.[Sebastiano B.],
Unsupervised Change Detection From Multichannel SAR Data by Markovian Data Fusion,
GeoRS(47), No. 7, July 2009, pp. 2114-2128.
IEEE DOI 0906
BibRef

Solarna, D.[David], Moser, G.[Gabriele], Serpico, S.B.[Sebastiano B.],
A Markovian Approach to Unsupervised Change Detection with Multiresolution and Multimodality SAR Data,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Chatelain, F., Tourneret, J.Y., Inglada, J., Ferrari, A.,
Bivariate Gamma Distributions for Image Registration and Change Detection,
IP(16), No. 7, July 2007, pp. 1796-1806.
IEEE DOI 0707
BibRef

Chatelain, F., Tourneret, J.Y., Inglada, J.,
Change Detection in Multisensor SAR Images Using Bivariate Gamma Distributions,
IP(17), No. 3, March 2008, pp. 249-258.
IEEE DOI 0802
BibRef

Inglada, J.[Jordi], Mercier, G.[Gregoire],
A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis,
GeoRS(45), No. 5, May 2007, pp. 1432-1445.
IEEE DOI 0704
SAR. BibRef

Slatton, K.C.[K. Clint], Crawford, M.M.[Melba M.], Chang, L.D.[Li-Der],
Modeling temporal variations in multipolarized radar scattering from intertidal coastal wetlands,
PandRS(63), No. 5, September 2008, pp. 559-577.
Elsevier DOI 0804
Polarization; SAR; LIDAR; Change detection; Coast BibRef

Gong, M., Zhou, Z., Ma, J.,
Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering,
IP(21), No. 4, April 2012, pp. 2141-2151.
IEEE DOI 1204
BibRef

Stojanovic, I.[Ivana], Novak, L.[Les],
Algorithms improve synthetic aperture radar coherent change detection performance,
SPIE(Newsroom), July 23, 2013
DOI Link 1310
A simple approach mitigates false alarms in coherent change detection algorithms, while still detecting subtle changes in synthetic aperture radar images. BibRef

Marino, A., Cloude, S.R., Lopez-Sanchez, J.M.,
A New Polarimetric Change Detector in Radar Imagery,
GeoRS(51), No. 5, May 2013, pp. 2986-3000.
IEEE DOI 1305
BibRef

Aghababaee, H.[Hossein], Amini, J.[Jalal], Tzeng, Y.C.[Yu-Chang], Sumantyo, J.T.S.[Josaphat Tetuko Sri],
Unsupervised Change Detection on SAR images using a New Fractal-Based Measure,
PFG(2013), No. 3, 2013, pp. 209-220.
DOI Link 1306
BibRef

Liu, F., Antoniou, M., Zeng, Z., Cherniakov, M.,
Coherent Change Detection Using Passive GNSS-Based BSAR: Experimental Proof of Concept,
GeoRS(51), No. 8, 2013, pp. 4544-4555.
IEEE DOI 1307
Charge coupled devices BibRef

Kreucher, C., Brennan, M.,
A Compressive Sensing Approach to Multistatic Radar Change Imaging,
GeoRS(52), No. 2, February 2014, pp. 1107-1112.
IEEE DOI 1402
antennas BibRef

Cha, M., Phillips, R.D., Wolfe, P.J., Richmond, C.D.,
Two-Stage Change Detection for Synthetic Aperture Radar,
GeoRS(53), No. 12, December 2015, pp. 6547-6560.
IEEE DOI 1512
image resolution BibRef

Zhang, Q.[Qilei], Antoniou, M., Chang, W.[Wenge], Cherniakov, M.,
Spatial Decorrelation in GNSS-Based SAR Coherent Change Detection,
GeoRS(53), No. 1, January 2015, pp. 219-228.
IEEE DOI 1410
Monte Carlo methods BibRef

Bryant, N., Bunch, W., Fretz, R., Kim, P., Logan, T., Smyth, M., Zobrist, A.,
Obtaining accurate change detection results from high-resolution satellite sensors,
AIPR12(1-5)
IEEE DOI 1307
aerosols BibRef

Marino, A., Hajnsek, I.,
A Change Detector Based on an Optimization With Polarimetric SAR Imagery,
GeoRS(52), No. 8, August 2014, pp. 4781-4798.
IEEE DOI 1403
Clutter BibRef

Marino, A., Hajnsek, I.,
Statistical Tests for a Ship Detector Based on the Polarimetric Notch Filter,
GeoRS(53), No. 8, August 2015, pp. 4578-4595.
IEEE DOI 1506
artificial satellites
See also Comments on Statistical Tests for a Ship Detector Based on the Polarimetric Notch Filter. BibRef

Gong, M.G.[Mao-Guo], Li, Y.[Yu], Jiao, L.C.[Li-Cheng], Jia, M.[Meng], Su, L.Z.[Lin-Zhi],
SAR change detection based on intensity and texture changes,
PandRS(93), No. 1, 2014, pp. 123-135.
Elsevier DOI 1407
Change detection BibRef

Liu, G.C.[Gan-Chao], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Zhong, H.[Hua], Wang, S.[Shuang],
A new patch based change detector for polarimetric SAR data,
PR(48), No. 3, 2015, pp. 685-695.
Elsevier DOI 1412
Change detection BibRef

Jia, L., Li, M., Wu, Y., Zhang, P., Liu, G., Chen, H., An, L.,
SAR Image Change Detection Based on Iterative Label-Information Composite Kernel Supervised by Anisotropic Texture,
GeoRS(53), No. 7, July 2015, pp. 3960-3973.
IEEE DOI 1503
Analytical models BibRef

Carotenuto, V., de Maio, A., Clemente, C., Soraghan, J.,
Invariant Rules for Multipolarization SAR Change Detection,
GeoRS(53), No. 6, June 2015, pp. 3294-3311.
IEEE DOI 1503
remote sensing by radar BibRef

Su, X.[Xin], Deledalle, C.A.[Charles-Alban], Tupin, F.[Florence], Sun, H.[Hong],
NORCAMA: Change analysis in SAR time series by likelihood ratio change matrix clustering,
PandRS(101), No. 1, 2015, pp. 247-261.
Elsevier DOI 1503
Change detection BibRef

Carotenuto, V., de Maio, A., Clemente, C., Soraghan, J.J., Alfano, G.,
Forcing Scale Invariance in Multipolarization SAR Change Detection,
GeoRS(54), No. 1, January 2016, pp. 36-50.
IEEE DOI 1601
decision theory BibRef

Pham, M.T.[Minh-Tan], Mercier, G., Michel, J.,
Change Detection Between SAR Images Using a Pointwise Approach and Graph Theory,
GeoRS(54), No. 4, April 2016, pp. 2020-2032.
IEEE DOI 1604
BibRef
Earlier:
A keypoint approach for change detection between SAR images based on graph theory,
MultiTemp15(1-4)
IEEE DOI 1511
feature extraction BibRef

Wahl, D.E., Yocky, D.A., Jakowatz, C.V., Simonson, K.M.,
A New Maximum-Likelihood Change Estimator for Two-Pass SAR Coherent Change Detection,
GeoRS(54), No. 4, April 2016, pp. 2460-2469.
IEEE DOI 1604
Charge coupled devices BibRef

Yang, W.[Wen], Yang, X., Yan, T., Song, H., Xia, G.S.[Gui-Song],
Region-Based Change Detection for Polarimetric SAR Images Using Wishart Mixture Models,
GeoRS(54), No. 11, November 2016, pp. 6746-6756.
IEEE DOI 1610
Computational modeling BibRef

Huang, X.J.[Xiao-Jing], Yang, W.[Wen], Xia, G.S.[Gui-Song], Liao, M.S.[Ming-Sheng],
Superpixel-based change detection in high resolution SAR images using region covariance features,
MultiTemp15(1-4)
IEEE DOI 1511
feature extraction BibRef

Song, H.[Hui], Yang, W.[Wen], Huang, X.J.[Xiao-Jing], Xu, X.[Xin],
Region-based change detection of PolSAR images using analytic information-theoretic divergence,
MultiTemp15(1-4)
IEEE DOI 1511
geophysical image processing BibRef

Yu, H.[Huai], Yang, W.[Wen], Hua, G.[Guang], Ru, H.[Hui], Huang, P.P.[Ping-Ping],
Change Detection Using High Resolution Remote Sensing Images Based on Active Learning and Markov Random Fields,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Ajadi, O.A.[Olaniyi A.], Meyer, F.J.[Franz J.], Webley, P.W.[Peter W.],
Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach,
RS(8), No. 6, 2016, pp. 482.
DOI Link 1608
BibRef

Akbari, V., Anfinsen, S.N., Doulgeris, A.P., Eltoft, T., Moser, G., Serpico, S.B.,
Polarimetric SAR Change Detection With the Complex Hotelling-Lawley Trace Statistic,
GeoRS(54), No. 7, July 2016, pp. 3953-3966.
IEEE DOI 1606
Computational modeling BibRef

Bouhlel, N.[Nizar], Akbari, V.[Vahid], Méric, S.[Stéphane],
Change Detection in Multilook Polarimetric SAR Imagery With Determinant Ratio Test Statistic,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI 2112
Covariance matrices, Light rail systems, Random variables, Synthetic aperture radar, Radar polarimetry, Speckle, Scattering, Wilks's lambda of the second kind distribution BibRef

Zheng, Y.G.[Yao-Guo], Jiao, L.C.[Li-Cheng], Liu, H.Y.[Hong-Ying], Zhang, X.R.[Xiang-Rong], Hou, B.[Biao], Wang, S.[Shuang],
Unsupervised saliency-guided SAR image change detection,
PR(61), No. 1, 2017, pp. 309-326.
Elsevier DOI 1705
Unsupervised change detection BibRef

Vu, V.T., Pettersson, M.I., Machado, R., Dammert, P., Hellsten, H.,
False Alarm Reduction in Wavelength-Resolution SAR Change Detection Using Adaptive Noise Canceler,
GeoRS(55), No. 1, January 2017, pp. 591-599.
IEEE DOI 1701
remote sensing by radar BibRef

Gao, F.[Feng], Liu, X.P.[Xiao-Peng], Dong, J.Y.[Jun-Yu], Zhong, G.Q.[Guo-Qiang], Jian, M.[Muwei],
Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Gong, M.G.[Mao-Guo], Yang, H.L.[Hai-Lun], Zhang, P.Z.[Pu-Zhao],
Feature learning and change feature classification based on deep learning for ternary change detection in SAR images,
PandRS(129), No. 1, 2017, pp. 212-225.
Elsevier DOI 1706
Ternary, change, detection BibRef

McGuinness, C., Balster, E.,
Enabling Reliable Change Detection for Independently Compressed SAR Images,
GeoRS(55), No. 8, August 2017, pp. 4785-4794.
IEEE DOI 1708
Charge coupled devices, Distortion, Image coding, Measurement, Quantization (signal), Synthetic aperture radar, Transform coding, Change detection algorithms, compression algorithms, synthetic, aperture, radar, (SAR) BibRef

Li, L., Zhao, Y., Sun, J., Stolkin, R., Pan, Q., Chan, J.C., Kong, S.G., Liu, Z.,
Deformable Dictionary Learning for SAR Image Change Detection,
GeoRS(56), No. 8, August 2018, pp. 4605-4617.
IEEE DOI 1808
image reconstruction, learning (artificial intelligence), object detection, radar imaging, remote sensing by radar, synthetic aperture radar (SAR) image BibRef

Zhuang, H.[Huifu], Fan, H.D.[Hong-Dong], Deng, K.[Kazhong], Yao, G.[Guobiao],
A Spatial-Temporal Adaptive Neighborhood-Based Ratio Approach for Change Detection in SAR Images,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Vu, V.T., Gomes, N.R., Pettersson, M.I., Dammert, P., Hellsten, H.,
Bivariate Gamma Distribution for Wavelength-Resolution SAR Change Detection,
GeoRS(57), No. 1, January 2019, pp. 473-481.
IEEE DOI 1901
Synthetic aperture radar, Probability density function, Clutter, Shape, Surveillance, Random variables, Bivariate gamma, CARABAS, synthetic aperture radar (SAR) BibRef

Gao, G.,
Comments on 'Statistical Tests for a Ship Detector Based on the Polarimetric Notch Filter',
GeoRS(57), No. 5, May 2019, pp. 3086-3087.
IEEE DOI 1905
notch filters, object detection, radar imaging, radar polarimetry, ships, statistical testing, synthetic aperture radar, synthetic aperture radar
See also Statistical Tests for a Ship Detector Based on the Polarimetric Notch Filter. BibRef

Cui, B.[Bin], Zhang, Y.H.[Yong-Hong], Yan, L.[Li], Wei, J.J.[Ju-Jie], Wu, H.[Hong'an],
An Unsupervised SAR Change Detection Method Based on Stochastic Subspace Ensemble Learning,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Domínguez, E.M.[E. Méndez], Magnard, C., Meier, E., Small, D., Schaepman, M.E., Henke, D.,
A Back-Projection Tomographic Framework for VHR SAR Image Change Detection,
GeoRS(57), No. 7, July 2019, pp. 4470-4484.
IEEE DOI 1907
Synthetic aperture radar, Tomography, Backscatter, Apertures, Laser radar, Image resolution, Detectors, Image processing, urban areas BibRef

Li, Y.Y.[Yang-Yang], Peng, C.[Cheng], Chen, Y.Q.[Yan-Qiao], Jiao, L.C.[Li-Cheng], Zhou, L.H.[Lin-Hao], Shang, R.H.[Rong-Hua],
A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images,
GeoRS(57), No. 8, August 2019, pp. 5751-5763.
IEEE DOI 1908
convolutional neural nets, fuzzy set theory, image classification, pattern clustering, radar computing, synthetic aperture radar (SAR) images BibRef

Yang, M.J.[Mei-Juan], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Hou, B.[Biao], Yang, S.Y.[Shu-Yuan],
Transferred Deep Learning-Based Change Detection in Remote Sensing Images,
GeoRS(57), No. 9, September 2019, pp. 6960-6973.
IEEE DOI 1909
Remote sensing, Task analysis, Image reconstruction, Feature extraction, Training, Adaptation models, Neural networks, remote sensing
See also Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval. BibRef

Chen, P.[Puhua], Guo, L.[Lei], Zhang, X.R.[Xiang-Rong], Qin, K.[Kai], Ma, W.T.[Wen-Tao], Jiao, L.C.[Li-Cheng],
Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Yang, M.J.[Mei-Juan], Jiao, L.C.[Li-Cheng], Hou, B.[Biao], Liu, F.[Fang], Yang, S.Y.[Shu-Yuan],
Selective Adversarial Adaptation-Based Cross-Scene Change Detection Framework in Remote Sensing Images,
GeoRS(59), No. 3, March 2021, pp. 2188-2203.
IEEE DOI 2103
Feature extraction, Remote sensing, Radar polarimetry, Adaptation models, Synthetic aperture radar, remote sensing images BibRef

Liu, G.C.[Gan-Chao], Li, L.L.[Ling-Ling], Jiao, L.C.[Li-Cheng], Dong, Y.S.[Yong-Sheng], Li, X.L.[Xue-Long],
Stacked Fisher autoencoder for SAR change detection,
PR(96), 2019, pp. 106971.
Elsevier DOI 1909
Stacked fisher autoencoder (SFAE), Synthetic aperture radar (SAR), Change detection, Fisher criterion BibRef

Peng, D.[Dong], Pan, T.[Ting], Yang, W.[Wen], Li, H.C.[Heng-Chao],
K-Matrix: A Novel Change-Pattern Mining Method for SAR Image Time Series,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Geng, J., Ma, X., Zhou, X., Wang, H.,
Saliency-Guided Deep Neural Networks for SAR Image Change Detection,
GeoRS(57), No. 10, October 2019, pp. 7365-7377.
IEEE DOI 1910
fuzzy set theory, geophysical image processing, geophysical techniques, image classification, neural nets, unsupervised learning BibRef

Samadi, F.[Farnaam], Akbarizadeh, G.[Gholamreza], Kaabi, H.[Hooman],
Change detection in SAR images using deep belief network: a new training approach based on morphological images,
IET-IPR(13), No. 12, October 2019, pp. 2255-2264.
DOI Link 1911
BibRef

Wan, L., Xiang, Y., You, H.,
An Object-Based Hierarchical Compound Classification Method for Change Detection in Heterogeneous Optical and SAR Images,
GeoRS(57), No. 12, December 2019, pp. 9941-9959.
IEEE DOI 1912
Image segmentation, Adaptive optics, Optical imaging, Radar polarimetry, Optical sensors, Optical distortion, Compounds, region-based Markov random field (MRF) model BibRef

Luo, B.[Bin], Hu, C.D.[Chu-Di], Su, X.[Xin], Wang, Y.J.[Ya-Jun],
Differentially Deep Subspace Representation for Unsupervised Change Detection of SAR Images,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Li, H.C.[Heng-Chao], Yang, G.[Gang], Yang, W.[Wen], Du, Q.[Qian], Emery, W.J.[William J.],
Deep nonsmooth nonnegative matrix factorization network with semi-supervised learning for SAR image change detection,
PandRS(160), 2020, pp. 167-179.
Elsevier DOI 2001
SAR image change detection, Nonsmooth nonnegative matrix factorization, Deep learning, Semi-supervised learning BibRef

Zhang, X.Z.[Xin-Zheng], Su, H.[Hang], Zhang, C.[Ce], Gu, X.W.[Xiao-Wei], Tan, X.O.[Xia-Oheng], Atkinson, P.M.[Peter M.],
Robust unsupervised small area change detection from SAR imagery using deep learning,
PandRS(173), 2021, pp. 79-94.
Elsevier DOI 2102
Change detection, Synthetic aperture radar, Difference image, Fuzzy c-means algorithm, Deep learning BibRef

Su, H.[Hang], Zhang, X.Z.[Xin-Zheng], Luo, Y.Q.[Yu-Qing], Zhang, C.[Ce], Zhou, X.C.[Xi-Chuan], Atkinson, P.M.[Peter M.],
Nonlocal feature learning based on a variational graph auto-encoder network for small area change detection using SAR imagery,
PandRS(193), 2022, pp. 137-149.
Elsevier DOI 2210
Synthetic aperture radar, Change detection, Difference image, Graph auto-encoder network, Deep learning BibRef

Zhang, X.Z.[Xin-Zheng], Liu, G.[Guo], Zhang, C.[Ce], Atkinson, P.M.[Peter M.], Tan, X.H.[Xiao-Heng], Jian, X.[Xin], Zhou, X.C.[Xi-Chuan], Li, Y.M.[Yong-Ming],
Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Li, Z.[Zhi], Jia, Z.H.[Zhen-Hong], liu, l.[luyang], Yang, J.[Jie], Kasabov, N.[Nikola],
A method to improve the accuracy of SAR image change detection by using an image enhancement method,
PandRS(163), 2020, pp. 137-151.
Elsevier DOI 2005
Image change detection, Combination of wavelet domain and spatial domain, FLCM BibRef

Wang, J.[Jun], Yang, X.Z.[Xue-Zhi], Yang, X.Y.[Xiang-Yu], Jia, L.[Lu], Fang, S.[Shuai],
Unsupervised change detection between SAR images based on hypergraphs,
PandRS(164), 2020, pp. 61-72.
Elsevier DOI 2005
Change detection, Synthetic aperture radar, Spatial-intensity correlation, Hypergraph matching, Hypergraph partition BibRef

Chen, J.W.[Jia-Wei], Wang, R.[Rongfang], Ding, F.[Fan], Liu, B.[Bo], Jiao, L.C.[Li-Cheng], Zhang, J.[Jie],
A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Ahmadi, S.[Salman], Homayouni, S.[Saeid],
A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Taillade, T.[Thibault], Thirion-Lefevre, L.[Laetitia], Guinvarc'h, R.[Régis],
Detecting Ephemeral Objects in SAR Time-Series Using Frozen Background-Based Change Detection,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Schwartz, C.[Christofer], Ramos, L.P.[Lucas P.], Duarte, L.T.[Leonardo T.], da S. Pinho, M.[Marcelo], Pettersson, M.I.[Mats I.], Vu, V.T.[Viet T.], Machado, R.[Renato],
Change Detection in UWB SAR Images Based on Robust Principal Component Analysis,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Ramos, L.P.[Lucas P.], Campos, A.B.[Alexandre B.], Schwartz, C.[Christofer], Duarte, L.T.[Leonardo T.], Alves, D.I.[Dimas I.], Pettersson, M.I.[Mats I.], Vu, V.T.[Viet T.], Machado, R.[Renato],
A Wavelength-Resolution SAR Change Detection Method Based on Image Stack through Robust Principal Component Analysis,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Bouhlel, N., Méric, S.,
Multilook Polarimetric SAR Change Detection Using Stochastic Distances Between Matrix-Variate Gd0 Distributions,
GeoRS(58), No. 10, October 2020, pp. 6823-6843.
IEEE DOI 2009
Stochastic processes, Covariance matrices, Synthetic aperture radar, Data models, kullback-Leibler BibRef

Liu, F., Tang, X., Zhang, X., Jiao, L., Liu, J.,
Large-Scope PolSAR Image Change Detection Based on Looking-Around-and-Into Mode,
GeoRS(59), No. 1, January 2021, pp. 363-378.
IEEE DOI 2012
Task analysis, Shape, Visualization, Image segmentation, Polarimetric synthetic aperture radar, Proposals, recurrent convolutional neural network (CNN) (Recurrent CNN) looseness-1 BibRef

Shu, Y.J.[Yuan-Jun], Li, W.[Wei], Yang, M.L.[Meng-Long], Cheng, P.[Peng], Han, S.C.[Song-Chen],
Patch-Based Change Detection Method for SAR Images with Label Updating Strategy,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Attioui, S.[Sanae], Najah, S.[Said],
Unsupervised change detection method in SAR images based on deep belief network using an improved fuzzy C-means clustering algorithm,
IET-IPR(15), No. 4, 2021, pp. 974-982.
DOI Link 2106
BibRef

Li, L.L.[Liang-Liang], Ma, H.B.[Hong-Bing], Jia, Z.H.[Zhen-Hong],
Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Hammer, H.[Horst], Kuny, S.[Silvia], Thiele, A.[Antje],
Enhancing Coherence Images for Coherent Change Detection: An Example on Vehicle Tracks in Airborne SAR Images,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Habibollahi, R.[Rezvan], Seydi, S.T.[Seyd Teymoor], Hasanlou, M.[Mahdi], Mahdianpari, M.[Masoud],
TCD-Net: A Novel Deep Learning Framework for Fully Polarimetric Change Detection Using Transfer Learning,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, J.[Jian], Wang, Y.H.[Ying-Hua], Liu, H.W.[Hong-Wei],
Hybrid Variability Aware Network (HVANet): A Self-Supervised Deep Framework for Label-Free SAR Image Change Detection,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Zhang, W.H.[Wen-Hua], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Yang, S.Y.[Shu-Yuan], Liu, J.[Jia],
Adaptive Contourlet Fusion Clustering for SAR Image Change Detection,
IP(31), 2022, pp. 2295-2308.
IEEE DOI 2203
Speckle, Radar polarimetry, Wavelet transforms, Neural networks, Clustering algorithms, Synthetic aperture radar, non-local clustering BibRef

Wang, Z.B.[Zhong-Bin], Wang, Y.[Yachao], Wang, B.N.[Bing-Nan], Xiang, M.S.[Mao-Sheng], Wang, R.R.[Rong-Rong], Xu, W.[Weidi], Song, C.[Chong],
Multi-Frequency Interferometric Coherence Characteristics Analysis of Typical Objects for Coherent Change Detection,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
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Huang, J.[Junnan], An, D.X.[Dao-Xiang], Luo, Y.X.[Yu-Xiao], Chen, J.W.[Jing-Wei], Zhou, Z.M.[Zhi-Min], Chen, L.[Leping], Feng, D.[Dong],
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Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Radar, SAR Registration .


Last update:Mar 16, 2024 at 20:36:19