4.12.2.11.1 PolSAR, Polarimetric SAR Classification, Targets

Bi, H., Sun, J., Xu, Z.,
Unsupervised PolSAR Image Classification Using Discriminative Clustering,
GeoRS(55), No. 6, June 2017, pp. 3531-3544.
IEEE DOI 1706
Algorithm design and analysis, Clustering algorithms, Feature extraction, Optimization, Scattering, Support vector machines, Training, Discriminative clustering, Markov random field (MRF), polarimetric synthetic aperture radar (PolSAR) image classification, softmax, regression, (SR), model BibRef

White, L.[Lori], Millard, K.[Koreen], Banks, S.[Sarah], Richardson, M.[Murray], Pasher, J.[Jon], Duffe, J.[Jason],
Moving to the RADARSAT Constellation Mission: Comparing Synthesized Compact Polarimetry and Dual Polarimetry Data with Fully Polarimetric RADARSAT-2 Data for Image Classification of Peatlands,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Tao, C.S.[Chen-Song], Chen, S.W.[Si-Wei], Li, Y.Z.[Yong-Zhen], Xiao, S.P.[Shun-Ping],
PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Dong, H., Xu, X., Sui, H., Xu, F., Liu, J.,
Copula-Based Joint Statistical Model for Polarimetric Features and Its Application in PolSAR Image Classification,
GeoRS(55), No. 10, October 2017, pp. 5777-5789.
IEEE DOI 1710
geophysical techniques, CoAS, PolSAR copula-based joint statistical model, real-valued polarimetric features, Correlation, Covariance matrices, Data models, Feature extraction, Scattering, BibRef

Chen, Y.Q.[Yan-Qiao], Jiao, L.C.[Li-Cheng], Li, Y.Y.[Yang-Yang], Zhao, J.[Jin],
Multilayer Projective Dictionary Pair Learning and Sparse Autoencoder for PolSAR Image Classification,
GeoRS(55), No. 12, December 2017, pp. 6683-6694.
IEEE DOI 1712
Dictionaries, Encoding, Feature extraction, Machine learning, Nonhomogeneous media, Scattering, sparse representation BibRef

Zhang, F.[Fan], Ni, J.[Jun], Yin, Q.A.[Qi-Ang], Li, W.[Wei], Li, Z.[Zheng], Liu, Y.F.[Yi-Fan], Hong, W.[Wen],
Nearest-Regularized Subspace Classification for PolSAR Imagery Using Polarimetric Feature Vector and Spatial Information,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Ni, J.[Jun], Zhang, F.[Fan], Yin, Q.A.[Qi-Ang], Li, H.C.[Heng-Chao],
Robust Weighting Nearest Regularized Subspace Classifier for PolSAR Imagery,
SPLetters(26), No. 10, October 2019, pp. 1496-1500.
IEEE DOI 1909
Training, Synthetic aperture radar, Feature extraction, Data mining, Scattering, Distance measurement, Polarimetric SAR, robust statistics BibRef

Chen, S.W.,
Polarimetric Coherence Pattern: A Visualization and Characterization Tool for PolSAR Data Investigation,
GeoRS(56), No. 1, January 2018, pp. 286-297.
IEEE DOI 1801
Coherence, Data visualization, Decorrelation, Radar polarimetry, Tools, Land cover classification, orientation diversity, synthetic aperture radar (SAR) BibRef

Song, W., Li, M., Zhang, P., Wu, Y., Tan, X., An, L.,
Mixture WG Gamma-MRF Model for PolSAR Image Classification,
GeoRS(56), No. 2, February 2018, pp. 905-920.
IEEE DOI 1802
Correlation, Covariance matrices, Data models, Image edge detection, Image segmentation, Mixture models, spatial-contextual information BibRef

Gou, S.P.[Shui-Ping], Qiao, X.[Xin], Zhang, X.R.[Xiang-Rong], Wang, W.F.[Wei-Fang], Du, F.F.[Fang-Fang],
Eigenvalue Analysis-Based Approach for POL-SAR Image Classification,
GeoRS(52), No. 2, February 2014, pp. 805-818.
IEEE DOI 1402
eigenvalues and eigenfunctions BibRef

Chen, W.S.[Wen-Shuai], Gou, S.P.[Shui-Ping], Wang, X.L.[Xin-Lin], Li, X.F.[Xiao-Feng], Jiao, L.C.[Li-Cheng],
Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Jiao, C.Z.[Chang-Zhe], Wang, X.L.[Xin-Lin], Gou, S.P.[Shui-Ping], Chen, W.S.[Wen-Shuai], Li, D.[Debo], Chen, C.[Chao], Li, X.F.[Xiao-Feng],
Self-Paced Convolutional Neural Network for PolSAR Images Classification,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Atwood, D.K., Thirion-Lefevre, L.,
Polarimetric Phase and Implications for Urban Classification,
GeoRS(56), No. 3, March 2018, pp. 1278-1289.
IEEE DOI 1804
Backscatter, Buildings, Radar polarimetry, Scattering, Synthetic aperture radar, Urban areas, Earth observing system, urban areas BibRef

Ren, B.[Bo], Hou, B.[Biao], Zhao, J.[Jin], Jiao, L.C.[Li-Cheng],
Sparse Subspace Clustering-Based Feature Extraction for PolSAR Imagery Classification,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Wang, Y.[Yan], He, C.[Chu], Liu, X.L.[Xin-Long], Liao, M.S.[Ming-Sheng],
A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

He, C.[Chu], Tu, M.X.[Ming-Xia], Xiong, D.[Dehui], Liao, M.S.[Ming-Sheng],
Nonlinear Manifold Learning Integrated with Fully Convolutional Networks for PolSAR Image Classification,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

He, C.[Chu], He, B.[Bokun], Tu, M.X.[Ming-Xia], Wang, Y.[Yan], Qu, T.[Tao], Wang, D.W.[Ding-Wen], Liao, M.S.[Ming-Sheng],
Fully Convolutional Networks and a Manifold Graph Embedding-Based Algorithm for PolSAR Image Classification,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Huang, X.Y.[Xia-Yuan], Qiao, H.[Hong], Zhang, B.[Bo], Nie, X.L.[Xiang-Li],
Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding,
IP(27), No. 6, June 2018, pp. 2966-2979.
IEEE DOI 1804
Covariance matrices, Dimensionality reduction, Feature extraction, Matrix decomposition, Speckle, tensor local discriminant embedding BibRef

Huang, X.Y.[Xia-Yuan],
PolSAR Image Classification Based-On Semi-Supervised Polarimetric Feature Selection,
ICIP23(196-200)
IEEE DOI 2312
BibRef

Huang, X.Y.[Xia-Yuan], Nie, X.L.[Xiang-Li],
Multi-View Feature Selection for PolSAR Image Classification via L_2,1 Sparsity Regularization and Manifold Regularization,
IP(30), 2021, pp. 8607-8618.
IEEE DOI 2110
Feature extraction, Manifolds, Correlation, Optimization, Synthetic aperture radar, Scattering, Matrix decomposition, manifold regularization BibRef

Hänsch, R.[Ronny], Hellwich, O.[Olaf],
Skipping the real world: Classification of PolSAR images without explicit feature extraction,
PandRS(140), 2018, pp. 122-132.
Elsevier DOI 1805
Random Forest, PolSAR, Classification, Feature learning BibRef

Liu, W.S.[Wen-Song], Yang, J.[Jie], Li, P.X.[Ping-Xiang], Han, Y.[Yue], Zhao, J.[Jinqi], Shi, H.T.[Hong-Tao],
A Novel Object-Based Supervised Classification Method with Active Learning and Random Forest for PolSAR Imagery,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Liu, C., Liao, W., Li, H., Fu, K., Philips, W.,
Unsupervised Classification of Multilook Polarimetric SAR Data Using Spatially Variant Wishart Mixture Model with Double Constraints,
GeoRS(56), No. 10, October 2018, pp. 5600-5613.
IEEE DOI 1810
Mixture models, Covariance matrices, Data models, Synthetic aperture radar, Correlation, Scattering, Task analysis, Wishart mixture model (WMM) BibRef

Wu, W., Li, H., Zhang, L., Li, X., Guo, H.,
High-Resolution PolSAR Scene Classification With Pretrained Deep Convnets and Manifold Polarimetric Parameters,
GeoRS(56), No. 10, October 2018, pp. 6159-6168.
IEEE DOI 1810
Data models, Backscatter, Synthetic aperture radar, Remote sensing, Image color analysis, Training, Optical sensors, synthetic aperture radar (SAR) BibRef

Li, D., Zhang, Y.,
Adaptive Model-Based Classification of PolSAR Data,
GeoRS(56), No. 12, December 2018, pp. 6940-6955.
IEEE DOI 1812
Scattering, Adaptation models, Silicon, Data models, Entropy, Radar polarimetry, Radar polarimetry, scattering model, unsupervised classification BibRef

Wu, Q., Hou, B., Wen, Z., Jiao, L.,
Variational Learning of Mixture Wishart Model for PolSAR Image Classification,
GeoRS(57), No. 1, January 2019, pp. 141-154.
IEEE DOI 1901
Data models, Computational modeling, Maximum likelihood estimation, Scattering, Training, Task analysis, variational Bayesian BibRef

Pallotta, L., de Maio, A., Orlando, D.,
A Robust Framework for Covariance Classification in Heterogeneous Polarimetric SAR Images and Its Application to L-Band Data,
GeoRS(57), No. 1, January 2019, pp. 104-119.
IEEE DOI 1901
Synthetic aperture radar, Covariance matrices, Symmetric matrices, Robustness, Data mining, Radar polarimetry, symmetry classification BibRef

Chen, Y.Q.[Yan-Qiao], Li, Y.Y.[Yang-Yang], Jiao, L.C.[Li-Cheng], Peng, C.[Cheng], Zhang, X.R.[Xiang-Rong], Shang, R.H.[Rong-Hua],
Adversarial Reconstruction-Classification Networks for PolSAR Image Classification,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Shang, R.H.[Rong-Hua], Wang, G.G.[Guang-Guang], Okoth, M.A.[Michael A.], Jiao, L.C.[Li-Cheng],
Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Chen, Y., Jiao, L., Li, Y., Li, L., Zhang, D., Ren, B., Marturi, N.,
A Novel Semicoupled Projective Dictionary Pair Learning Method for PolSAR Image Classification,
GeoRS(57), No. 4, April 2019, pp. 2407-2418.
IEEE DOI 1904
image classification, image representation, learning (artificial intelligence), radar imaging, stacked auto-encoder (SAE) BibRef

Wang, R.C.[Rui-Chuan], Wang, Y.F.[Yan-Fei],
Classification of PolSAR Image Using Neural Nonlocal Stacked Sparse Autoencoders with Virtual Adversarial Regularization,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

West, R.D., Riley, R.M.,
Polarimetric Interferometric SAR Change Detection Discrimination,
GeoRS(57), No. 6, June 2019, pp. 3091-3104.
IEEE DOI 1906
Synthetic aperture radar, Charge coupled devices, Coherence, Matrix decomposition, Radar cross-sections, Laboratories, probabilistic feature fusion (PFF) model BibRef

Koch, M.W., West, R.D., Riley, R.M., Quach, T.,
Polarimetric Synthetic-Aperture-Radar Change-Type Classification with a Hyperparameter-Free Open-Set Classifier,
PBVS18(1320-1327)
IEEE DOI 1812
Synthetic aperture radar, Coherence, Charge coupled devices, Radar tracking, Matrix decomposition, Vegetation BibRef

Zhang, X.Z.[Xin-Zheng], Xia, J.[Jili], Tan, X.H.[Xiao-Heng], Zhou, X.C.[Xi-Chuan], Wang, T.[Tao],
PolSAR Image Classification via Learned Superpixels and QCNN Integrating Color Features,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Li, Y.Y.[Yang-Yang], Xing, R.T.[Ruo-Ting], Jiao, L.C.[Li-Cheng], Chen, Y.Q.[Yan-Qiao], Chai, Y.T.[Ying-Te], Marturi, N.[Naresh], Shang, R.H.[Rong-Hua],
Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Jiao, L.C.[Li-Cheng], Liu, F.[Fang],
Wishart Deep Stacking Network for Fast POLSAR Image Classification,
IP(25), No. 7, July 2016, pp. 3273-3286.
IEEE DOI 1606
image classification BibRef

Li, Y.Y.[Yang-Yang], Chen, Y.Q.[Yan-Qiao], Liu, G.Y.[Guang-Yuan], Jiao, L.C.[Li-Cheng],
A Novel Deep Fully Convolutional Network for PolSAR Image Classification,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Xie, W.[Wen], Jiao, L.C.[Li-Cheng], Hua, W.Q.[Wen-Qiang],
Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Hua, W.Q.[Wen-Qiang], Wang, Y.[Yi], Yang, S.[Sijia], Jin, X.M.[Xiao-Min],
PolSAR Image Classification Based on Multi-Modal Contrastive Fully Convolutional Network,
RS(16), No. 2, 2024, pp. 296.
DOI Link 2402
BibRef

Hua, W.Q.[Wen-Qiang], Zhang, Y.R.[Yu-Rong], Zhang, C.[Cong], Jin, X.M.[Xiao-Min],
PolSAR Image Classification Based on Relation Network with SWANet,
RS(15), No. 8, 2023, pp. 2025.
DOI Link 2305
BibRef

Liu, X.[Xu], Jiao, L.C.[Li-Cheng], Tang, X.[Xu], Sun, Q.G.[Qi-Gong], Zhang, D.[Dan],
Polarimetric Convolutional Network for PolSAR Image Classification,
GeoRS(57), No. 5, May 2019, pp. 3040-3054.
IEEE DOI 1905
convolution, covariance matrices, image classification, radar imaging, radar polarimetry, synthetic aperture radar, polarimetric synthetic aperture radar (PolSAR) BibRef

Liu, F.[Fang], Jiao, L.C.[Li-Cheng], Hou, B.[Biao], Yang, S.Y.[Shu-Yuan],
POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information,
GeoRS(54), No. 6, June 2016, pp. 3292-3308.
IEEE DOI 1606
belief networks BibRef

Hou, B.[Biao], Wang, J.L.[Jian-Long], Jiao, L.C.[Li-Cheng], Wang, S.[Shuang],
Auto Encoder Feature Learning with Utilization of Local Spatial Information and Data Distribution for Classification of PolSAR Image,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Guo, Y.W.[Yu-Wei], Sun, Z.Z.[Zhuang-Zhuang], Qu, R.[Rong], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Zhang, X.R.[Xiang-Rong],
Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Mohanty, S., Singh, G.,
Improved POLSAR Model-Based Decomposition Interpretation Under Scintillation Conditions,
GeoRS(57), No. 10, October 2019, pp. 7567-7578.
IEEE DOI 1910
fast Fourier transforms, phased array radar, radar imaging, radar polarimetry, radar receivers, remote sensing by radar, supervised classification BibRef

Yang, C., Hou, B., Ren, B., Hu, Y., Jiao, L.,
CNN-Based Polarimetric Decomposition Feature Selection for PolSAR Image Classification,
GeoRS(57), No. 11, November 2019, pp. 8796-8812.
IEEE DOI 1911
Feature extraction, Scattering, Matrix decomposition, Covariance matrices, Task analysis, Indexes, Data mining, polarimetric target decomposition BibRef

Wen, Z., Wu, Q., Liu, Z., Pan, Q.,
Polar-Spatial Feature Fusion Learning With Variational Generative-Discriminative Network for PolSAR Classification,
GeoRS(57), No. 11, November 2019, pp. 8914-8927.
IEEE DOI 1911
Feature extraction, Data models, Scattering, Task analysis, Covariance matrices, Adaptation models, Deep learning, variational inference BibRef

Bi, H., Xu, F., Wei, Z., Xue, Y., Xu, Z.,
An Active Deep Learning Approach for Minimally Supervised PolSAR Image Classification,
GeoRS(57), No. 11, November 2019, pp. 9378-9395.
IEEE DOI 1911
Deep learning, Neural networks, Training, Synthetic aperture radar, Remote sensing, Learning systems, Task analysis, Active learning, polarimetric synthetic aperture radar (PolSAR) image classification BibRef

Zhang, L.[Lamei], Dong, H.W.[Hong-Wei], Zou, B.[Bin],
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework,
PandRS(157), 2019, pp. 59-72.
Elsevier DOI 1911
Deep learning, Convolutional neural networks, Polarimetric synthetic aperture radar (PolSAR) classification, Depthwise separable convolutions BibRef

Cao, Y.[Yice], Wu, Y.[Yan], Zhang, P.[Peng], Liang, W.K.[Wen-Kai], Li, M.[Ming],
Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Network,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Yin, J., Papathanassiou, K.P., Yang, J.,
Formalism of Compact Polarimetric Descriptors and Extension of the Delta-alpha_B/alpha_B Method for General Compact-Pol SAR,
GeoRS(57), No. 12, December 2019, pp. 10322-10335.
IEEE DOI 1912
Scattering, Synthetic aperture radar, Backscatter, Standards, Imaging, Radar imaging, Radar polarimetry, Classification, target decomposition BibRef

Gadhiya, T., Roy, A.K.,
Superpixel-Driven Optimized Wishart Network for Fast PolSAR Image Classification Using Global k-Means Algorithm,
GeoRS(58), No. 1, January 2020, pp. 97-109.
IEEE DOI 2001
Matrix decomposition, Scattering, Synthetic aperture radar, Microwave imaging, Microwave theory and techniques, revised Wishart distance (RWD) BibRef

Dong, H.W.[Hong-Wei], Zhang, L.M.[La-Mei], Zou, B.[Bin],
PolSAR Image Classification with Lightweight 3D Convolutional Networks,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Wu, Q.[Qian], Hou, B.[Biao], Wen, Z.[Zaidao], Ren, Z.L.[Zhong-Le], Ren, B.[Bo], Jiao, L.C.[Li-Cheng],
Structure Label Matrix Completion for PolSAR Image Classification,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Wang, J., Hou, B., Jiao, L., Wang, S.,
POL-SAR Image Classification Based on Modified Stacked Autoencoder Network and Data Distribution,
GeoRS(58), No. 3, March 2020, pp. 1678-1695.
IEEE DOI 2003
Autoencoder (AE) network, classification network, coherency matrix, image classification, Wishart distribution BibRef

Li, L., Zeng, J., Jiao, L., Liang, P., Liu, F., Yang, S.,
Online Active Extreme Learning Machine With Discrepancy Sampling for PolSAR Classification,
GeoRS(58), No. 3, March 2020, pp. 2027-2041.
IEEE DOI 2003
Extreme learning machine (ELM), margin sampling (MS), online active extreme learning machine (OA-ELM) algorithm, polarimetric synthetic aperture radar (PolSAR) classification BibRef

Yin, J.J.[Jun-Jun], Liu, X.Y.[Xi-Yun], Yang, J.[Jian], Chu, C.Y.[Chih-Yuan], Chang, Y.L.[Yang-Lang],
PolSAR Image Classification Based on Statistical Distribution and MRF,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Li, L.L.[Ling-Ling], Ma, L.Y.[Li-Yuan], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Sun, Q.[Qigong], Zhao, J.[Jin],
Complex Contourlet-CNN for polarimetric SAR image classification,
PR(100), 2020, pp. 107110.
Elsevier DOI 2005
Complex Contourlet-CNN, Multiscale deep Contourlet filter banks, Polarimetric SARimage classification BibRef

Liu, H.Y.[Hong-Ying], Luo, R.[Ruyi], Shang, F.[Fanhua], Meng, X.C.[Xue-Chun], Gou, S.P.[Shui-Ping], Hou, B.[Biao],
Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Wang, S., Guo, Y., Hua, W., Liu, X., Song, G., Hou, B., Jiao, L.,
Semi-Supervised PolSAR Image Classification Based on Improved Tri-Training With a Minimum Spanning Tree,
GeoRS(58), No. 12, December 2020, pp. 8583-8597.
IEEE DOI 2012
Reliability, Training, Matrix decomposition, Scattering, Clustering algorithms, Lead, Image color analysis, Tri-training BibRef

Wang, L.[Lei], Xu, X.[Xin], Gui, R.[Rong], Yang, R.[Rui], Pu, F.L.[Fang-Ling],
Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Cao, Y.[Yice], Wu, Y.[Yan], Li, M.[Ming], Liang, W.K.[Wen-Kai], Zhang, P.[Peng],
PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Zhang, Y.C.[Ya-Chao], Lai, X.[Xuan], Xie, Y.[Yuan], Qu, Y.Y.[Yan-Yun], Li, C.H.[Cui-Hua],
Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Wang, J.L.[Jian-Long], Hou, B.[Biao], Jiao, L.C.[Li-Cheng], Wang, S.[Shuang],
Representative Learning via Span-Based Mutual Information for PolSAR Image Classification,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Wu, Q.[Qian], Hou, B.[Biao], Wen, Z.D.[Zai-Dao], Ren, Z.L.[Zhong-Le], Jiao, L.C.[Li-Cheng],
Cost-Sensitive Latent Space Learning for Imbalanced PolSAR Image Classification,
GeoRS(59), No. 6, June 2021, pp. 4802-4817.
IEEE DOI 2106
Task analysis, Scattering, Feature extraction, Matrix converters, Polarimetric synthetic aperture radar, Support vector machines, multi-task learning BibRef

Ren, B.[Bo], Zhao, Y.Y.[Yang-Yang], Hou, B.[Biao], Chanussot, J.[Jocelyn], Jiao, L.C.[Li-Cheng],
A Mutual Information-Based Self-Supervised Learning Model for PolSAR Land Cover Classification,
GeoRS(59), No. 11, November 2021, pp. 9224-9237.
IEEE DOI 2111
Feature extraction, Task analysis, Data models, Data mining, Scattering, Mutual information, Covariance matrices, self-supervised learning (SSL) BibRef

Gui, R.[Rong], Xu, X.[Xin], Yang, R.[Rui], Wang, L.[Lei], Pu, F.L.[Fang-Ling],
Statistical Scattering Component-Based Subspace Alignment for Unsupervised Cross-Domain PolSAR Image Classification,
GeoRS(59), No. 7, July 2021, pp. 5449-5463.
IEEE DOI 2106
Scattering, Synthetic aperture radar, Sensors, Radar imaging, Image sensors, Remote sensing, Land-cover classification, unsupervised domain adaptation (DA) BibRef

Ni, J.[Jun], Zhang, F.[Fan], Yin, Q.[Qiang], Zhou, Y.S.[Yong-Sheng], Li, H.C.[Heng-Chao], Hong, W.[Wen],
Random Neighbor Pixel-Block-Based Deep Recurrent Learning for Polarimetric SAR Image Classification,
GeoRS(59), No. 9, September 2021, pp. 7557-7569.
IEEE DOI 2109
Feature extraction, Training, Covariance matrices, Matrix decomposition, Data mining, Scattering, Training data, polarimetric synthetic aperture radar (PolSAR) BibRef

Tan, X.F.[Xiao-Feng], Li, M.[Ming], Zhang, P.[Peng], Wu, Y.[Yan], Song, W.Y.[Wan-Ying],
Deep Triplet Complex-Valued Network for PolSAR Image Classification,
GeoRS(59), No. 12, December 2021, pp. 10179-10196.
IEEE DOI 2112
Feature extraction, Synthetic aperture radar, Covariance matrices, Neural networks, Measurement, polarimetric synthetic aperture radar (PolSAR) BibRef

Jafarzadeh, H.[Hamid], Mahdianpari, M.[Masoud], Gill, E.[Eric], Mohammadimanesh, F.[Fariba], Homayouni, S.[Saeid],
Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Qin, X.X.[Xian-Xiang], Zou, H.X.[Huan-Xin], Yu, W.[Wangsheng], Wang, P.[Peng],
Superpixel-Oriented Classification of PolSAR Images Using Complex-Valued Convolutional Neural Network Driven by Hybrid Data,
GeoRS(59), No. 12, December 2021, pp. 10094-10111.
IEEE DOI 2112
Feature extraction, Training, Task analysis, Data models, Data mining, Radar polarimetry, superpixel regularization BibRef

Wang, J.L.[Jian-Long], Hou, B.[Biao], Ren, B.[Bo], Zhang, Y.[Yake], Yang, M.J.[Mei-Juan], Wang, S.[Shuang], Jiao, L.C.[Li-Cheng],
Parameter selection of Touzi decomposition and a distribution improved autoencoder for PolSAR image classification,
PandRS(186), 2022, pp. 246-266.
Elsevier DOI 2203
Polarization decomposition, Data distribution, Touzi decomposition, Parameter selection, Feature extraction, Autoencoder network BibRef

Cui, Y.H.[Yuan-Hao], Liu, F.[Fang], Liu, X.[Xu], Li, L.L.[Ling-Ling], Qian, X.X.[Xiao-Xue],
TCSPANet: Two-Staged Contrastive Learning and Sub-Patch Attention Based Network for PolSAR Image Classification,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Ren, B.[Bo], Chen, M.Q.[Meng-Qian], Hou, B.[Biao], Hong, D.F.[Dan-Feng], Ma, S.B.[Shi-Bin], Chanussot, J.[Jocelyn], Jiao, L.C.[Li-Cheng],
PolSAR Scene Classification via Low-Rank Constrained Multimodal Tensor Representation,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Han, B.B.[Bin-Bin], Han, P.[Ping], Cheng, Z.[Zheng],
Object-Oriented Unsupervised Classification of PolSAR Images Based on Image Block,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Sun, J.[Jili], Geng, L.[Lingdong], Wang, Y.Z.[Yi-Ze],
A Hybrid Model Based on Superpixel Entropy Discrimination for PolSAR Image Classification,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Imani, M.[Maryam],
Polarimetric SAR image classification using binary coding-based polarimetric-morphological features,
IET-IPR(16), No. 14, 2022, pp. 3715-3736.
DOI Link 2212
BibRef

Yao, H.[Hang], Fu, B.[Bolin], Zhang, Y.[Ya], Li, S.Z.[Sun-Zhe], Xie, S.Y.[Shu-Yu], Qin, J.L.[Jiao-Ling], Fan, D.L.[Dong-Lin], Gao, E.[Ertao],
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Liu, M.L.[Ming-Liang], Deng, Y.K.[Yun-Kai], Han, C.Z.[Chuan-Zhao], Hou, W.T.[Wen-Tao], Gao, Y.[Yao], Wang, C.L.[Chun-Le], Liu, X.Q.[Xiu-Qing],
An Innovative Supervised Classification Algorithm for PolSAR Image Based on Mixture Model and MRF,
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Tang, R.[Rui], Pu, F.L.[Fang-Ling], Yang, R.[Rui], Xu, Z.Z.[Zhao-Zhuo], Xu, X.[Xin],
Multi-Domain Fusion Graph Network for Semi-Supervised PolSAR Image Classification,
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Radman, A.[Ali], Mahdianpari, M.[Masoud], Brisco, B.[Brian], Salehi, B.[Bahram], Mohammadimanesh, F.[Fariba],
Dual-Branch Fusion of Convolutional Neural Network and Graph Convolutional Network for PolSAR Image Classification,
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Zhang, C.[Chuang], Mu, Y.X.[Ya-Xin], Xia, Z.H.[Zheng-Huan], Jin, S.C.[Shi-Chao], Yue, F.Z.[Fu-Zhan], Liu, X.[Xin], Zhang, L.Q.[Lan-Qing], Tian, Z.X.[Zhi-Xin], Liu, Z.Q.[Zong-Qiang], Zhang, Y.[Yao], Gao, W.N.[Wen-Ning], Zhang, T.[Tao], Zhao, Z.L.[Zhi-Long], Zhang, Y.[Ying],
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Li, Z.C.[Ze-Chen], Li, H.C.[Heng-Chao], Gao, G.[Gui], Hua, Z.X.[Ze-Xi], Zhang, F.[Fan], Hong, W.[Wen],
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Polarimetric synthetic aperture radar, Unsupervised classification, Variational inference, Remote sensing BibRef

Dong, H.W.[Hong-Wei], Si, L.Y.[Ling-Yu], Qiang, W.W.[Wen-Wen], Miao, W.[Wuxia], Zheng, C.[Changwen], Wu, Y.[Yuquan], Zhang, L.[Lamei],
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Polarimetric projection, Scattering characteristics extraction, Polarimetric SAR BibRef

Shi, J.F.[Jun-Fei], Nie, M.M.[Meng-Meng], Ji, S.S.[Shan-Shan], Shi, C.[Cheng], Liu, H.Y.[Hong-Ying], Jin, H.Y.[Hai-Yan],
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ICIP19(634-638)
IEEE DOI 1910
multiple features, MCCA, MSE, feature extraction, PolSAR image classification BibRef

Nie, X., Luo, Y., Qiao, H., Zhang, B., Jiang, Z.,
An Incremental Multi-view Active Learning Algorithm for PolSAR Data Classification,
ICPR18(2251-2255)
IEEE DOI 1812
Heuristic algorithms, Prediction algorithms, Task analysis, Data models, Feature extraction, Learning systems, Polarimetric synthetic aperture radar BibRef

Nie, X., Ding, S., Zhang, B., Qiao, H., Huang, X.,
Polsar data online classification based on multi-view learning,
ICIP17(2354-2358)
IEEE DOI 1803
Feature extraction, Image color analysis, Optimization, Polarimetric synthetic aperture radar, Prediction algorithms, polarimetric synthetic aperture radar (PolSAR) BibRef

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Hierarchical semantic model BibRef

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Parameters analysis for polarimetric SAR Based on classification accuracy,
IASP10(268-271).
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Cai, A.[Aimin], Shao, Y.[Yun], Gong, H.[Huaze],
Parameters extraction of crop based on PolSAR Data,
IASP10(12-15).
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Classification of Polarimetric SAR Data by Complex Valued Neural Networks,
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Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Motion Compensation for Radar .

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