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.H.[Pu-Hua],
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
BibRef
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],
A Registration Method for Dual-Frequency, High-Spatial-Resolution SAR
Images,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Mastro, P.[Pietro],
Masiello, G.[Guido],
Serio, C.[Carmine],
Pepe, A.[Antonio],
Change Detection Techniques with Synthetic Aperture Radar Images:
Experiments with Random Forests and Sentinel-1 Observations,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Xuan, J.[Jiayu],
Xin, Z.H.[Zhi-Hui],
Liao, G.S.[Gui-Sheng],
Huang, P.H.[Peng-Hui],
Wang, Z.X.[Zhi-Xu],
Sun, Y.[Yu],
Change Detection Based on Fusion Difference Image and Multi-Scale
Morphological Reconstruction for SAR Images,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Jiang, A.[Aihui],
Dai, J.[Jie],
Yu, S.[Sisi],
Zhang, B.L.[Bao-Lei],
Xie, Q.Y.[Qiao-Yun],
Zhang, H.X.[Huan-Xue],
Unsupervised Change Detection around Subways Based on SAR Combined
Difference Images,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Xia, Y.[Yufa],
Xu, X.[Xin],
Pu, F.L.[Fang-Ling],
PCBA-Net: Pyramidal Convolutional Block Attention Network for
Synthetic Aperture Radar Image Change Detection,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Liu, W.[Wei],
Lin, Z.K.[Zhi-Kang],
Gao, G.[Gui],
Niu, C.Y.[Chao-Yang],
Lu, W.J.[Wan-Jie],
Unsupervised SAR Image Change Type Recognition Using Regionally
Restricted PCA-Kmean and Lightweight MobileNet,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Du, Y.[Yu],
Zhong, R.F.[Ruo-Fei],
Li, Q.Y.[Qing-Yang],
Zhang, F.[Furao],
TransUNet++SAR: Change Detection with Deep Learning about
Architectural Ensemble in SAR Images,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Zhang, K.Y.[Kai-Yu],
Lv, X.L.[Xiao-Lei],
Guo, B.[Bin],
Chai, H.M.[Hui-Ming],
Unsupervised SAR Image Change Detection Based on Histogram Fitting
Error Minimization and Convolutional Neural Network,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Zhu, J.X.[Jing-Xing],
Wang, F.[Feng],
You, H.J.[Hong-Jian],
Unsupervised SAR Image Change Detection Based on Structural
Consistency and CFAR Threshold Estimation,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Ji, L.[Linxia],
Zhao, J.Q.[Jin-Qi],
Zhao, Z.[Zheng],
A Novel End-to-End Unsupervised Change Detection Method with
Self-Adaptive Superpixel Segmentation for SAR Images,
RS(15), No. 7, 2023, pp. 1724.
DOI Link
2304
BibRef
Zhang, J.L.[Jian-Long],
Liu, Y.F.[Yi-Fan],
Wang, B.[Bin],
Chen, C.[Chen],
A Hierarchical Fusion SAR Image Change-Detection Method Based on
HF-CRF Model,
RS(15), No. 11, 2023, pp. 2741.
DOI Link
2306
BibRef
Zhang, P.J.[Pei-Jing],
Jiang, J.B.[Jin-Bao],
Kou, P.[Peng],
Wang, S.[Shining],
Wang, B.[Bin],
A Multi-Scale Graph Based on Spatio-Temporal-Radiometric Interaction
for SAR Image Change Detection,
RS(16), No. 3, 2024, pp. 560.
DOI Link
2402
BibRef
Li, L.L.[Liang-Liang],
Ma, H.B.[Hong-Bing],
Zhang, X.[Xueyu],
Zhao, X.B.[Xia-Bin],
Lv, M.[Ming],
Jia, Z.H.[Zhen-Hong],
Synthetic Aperture Radar Image Change Detection Based on Principal
Component Analysis and Two-Level Clustering,
RS(16), No. 11, 2024, pp. 1861.
DOI Link
2406
BibRef
Wang, Y.[Ying],
Dang, K.[Kelin],
Yang, R.[Rennong],
Song, Q.[Qi],
Li, H.[Hao],
Gong, M.[Maoguo],
Self-Paced Multi-Scale Joint Feature Mapper for Multi-Objective
Change Detection in Heterogeneous Images,
RS(16), No. 11, 2024, pp. 1961.
DOI Link
2406
BibRef
Mullissa, A.[Adugna],
Saatchi, S.[Sassan],
Dalagnol, R.[Ricardo],
Erickson, T.[Tyler],
Provost, N.[Naomi],
Osborn, F.[Fiona],
Ashary, A.[Aleena],
Moon, V.[Violet],
Melling, D.[Daniel],
LUCA: A Sentinel-1 SAR-Based Global Forest Land Use Change Alert,
RS(16), No. 12, 2024, pp. 2151.
DOI Link
2406
BibRef
Yuan, M.T.[Meng-Ting],
Xin, Z.H.[Zhi-Hui],
Liao, G.S.[Gui-Sheng],
Huang, P.H.[Peng-Hui],
Li, Y.X.[Yong-Xin],
Change detection in SAR image based on weighted difference image
generation and optimized random forest,
IET-IPR(18), No. 10, 2024, pp. 2754-2773.
DOI Link
2408
decision trees, image processing, image segmentation,
random forests, synthetic aperture radar
BibRef
Manzoni, M.,
Monti-Guarnieri, A.,
Molinari, M.E.,
High-resolution SAR Coherent Change Detection In Urban Environment,
ISPRS20(B3:1569-1575).
DOI Link
2012
BibRef
Seydi, S.T.,
Shahhoseini, R.,
Transformation Based Algorithms for Change Detection in Full
Polarimetric Remote Sensing Images,
SMPR19(963-967).
DOI Link
1912
BibRef
Yousefizadeh, L.,
Shahhoseini, R.,
Homayouni, S.,
Full Polarimetric Uavsar Image Change Detection Based On Change Indices,
SMPR19(1107-1111).
DOI Link
1912
BibRef
Hu, H.[Hao],
Liu, B.[Bin],
Guo, W.W.[Wei-Wei],
Zhang, Z.H.[Zeng-Hui],
Yu, W.X.[Wen-Xian],
Preliminary exploration of introducing spatial correlation
information into the probabilistic patch-based similarity measure,
MultiTemp17(1-4)
IEEE DOI
1712
correlation methods, probability, radar imaging, radar tracking,
synthetic aperture sonar, SAR image change detection,
Synthetic aperture radar
BibRef
Garzelli, A.,
Zoppetti, C.,
Optimizing SAR change detection based on log-ratio features,
MultiTemp17(1-4)
IEEE DOI
1712
image filtering, parameter estimation, remote sensing by radar,
statistical analysis, synthetic aperture radar,
Synthetic aperture radar
BibRef
Bouhlel, N.,
Ginolhac, G.,
Jolibois, E.,
Atto, A.,
Multivariate statistical modeling for multi-temporal SAR change
detection using wavelet transforms,
MultiTemp15(1-4)
IEEE DOI
1511
Gaussian distribution
BibRef
Quach, T.T.[Tu-Thach],
Malinas, R.[Rebecca],
Koch, M.W.[Mark W.],
A model-based approach to finding tracks in SAR CCD images,
PBVS15(41-47)
IEEE DOI
1510
coherent change detection images.
Charge coupled devices.
Tire tracks, etc.
BibRef
Sun, X.,
Zhang, J.,
Zhai, L.,
Multipolarimetric SAR Image Change Detection Based on Multiscale
Feature-Level Fusion,
IWIDF15(155-158).
DOI Link
1508
BibRef
Dominguez, E.M.[E. Mendez],
Henke, D.,
Small, D.,
Meier, E.,
High Resolution Airborne SAR Image Change Detection in Urban Areas with
Slightly Different Acquisition Geometries,
PIA15(127-133).
DOI Link
1504
BibRef
Boldt, M.,
Thiele, A.,
Schulz, K.,
Hinz, S.,
SAR Image Segmentation Using Morphological Attribute Profiles,
PCV14(39-44).
DOI Link
1404
BibRef
Earlier: A1, A3, A2, A4:
Using Morphological Differential Attribute Profiles for Change
Categorization in High Resolution SAR Images,
Hannover13(29-34).
DOI Link
1308
BibRef
Krylov, V.A.[Vladimir A.],
Moser, G.[Gabriele],
Voisin, A.[Aurelie],
Serpico, S.B.[Sebastiano B.],
Zerubia, J.B.[Josiane B.],
Change detection with synthetic aperture radar images by Wilcoxon
statistic likelihood ratio test,
ICIP12(2093-2096).
IEEE DOI
1302
BibRef
Hachicha, S.[Sofiane],
Chaabane, F.[Ferdaous],
Multi-temporal Sar Change Detection And Monitoring,
ISPRS12(XXXIX-B7:293-298).
DOI Link
1209
BibRef
Liu, M.[Meng],
Zhang, H.[Hong],
Wang, C.[Chao],
Tang, Y.X.[Yi-Xian],
PolSAR change detection for specific land cover type by testing
equality of two PolInSAR coherency matrixes,
CVRS12(371-376).
IEEE DOI
1302
BibRef
Hachicha, S.[Sofiane],
Deledalle, C.A.[Charles-Alban],
Chaabane, F.[Ferdaous],
Tupin, F.[Florence],
Multi-temporal SAR classification according to change detection
operators,
MultiTemp11(133-136).
IEEE DOI
1109
BibRef
Gromek, A.,
Jenerowicz, M.,
SAR imagery change detection method for Land Border Monitoring,
MultiTemp11(213-216).
IEEE DOI
1109
BibRef
Hachicha, S.,
Chaabane, F.,
Application of DSM theory for SAR image change detection,
ICIP09(3733-3736).
IEEE DOI
0911
BibRef
Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Radar, SAR Registration .