Novak, L.M.,
Owirka, G.J., and
Netishen, C.M.,
Performance of a High-Resolution Polarimetric SAR Automatic Target
Recogniton System,
LLJ(6), No. 1 1993, pp. 11-23.
SAR.
BibRef
9300
Novak, L.M.[Leslie M.],
Hesse, S.R.[Steven R.], O
Optimal Polarizations for Radar Detection and Recognition of
Targets in Clutter,
SPIE(1700), 1992, pp. 114-118.
BibRef
9200
Hauter, A.[Andrew],
Chang, K.C.[Kuo Chu],
Karp, S.[Sherman],
Polarimetric Fusion for Synthetic-Aperture Radar Target Classification,
PR(30), No. 5, May 1997, pp. 769-775.
Elsevier DOI
9705
BibRef
Fukuda, S.,
Hirosawa, H.,
A Wavelet-Based Texture Feature Set Applied to Classification of
Multifrequency Polarimetric SAR Images,
GeoRS(37), No. 5, September 1999, pp. 2282.
IEEE Top Reference.
BibRef
9909
Chen, C.T.[Chia-Tang],
Chen, K.S.[Kun-Shan],
Lee, J.S.[Jong-Sen],
The use of fully polarimetric information for the fuzzy neural
classification of SAR images,
GeoRS(41), No. 9, September 2003, pp. 2089-2100.
IEEE Abstract.
0310
BibRef
Souyris, J.C.,
Henry, C.,
Adragna, F.,
On the Use of Complex SAR Image Spectral Analysis for Target Detection:
Assessment of Polarimetry,
GeoRS(41), No. 12, December 2003, pp. 2725-2734.
IEEE Abstract.
0402
BibRef
Nashashibi, A.Y.,
Ulaby, F.T.,
Detection of Stationary Foliage-Obscured Targets by Polarimetric
Millimeter-Wave Radar,
GeoRS(43), No. 1, January 2005, pp. 13-23.
IEEE Abstract.
0501
BibRef
Lardeux, C.,
Frison, P.L.,
Tison, C.,
Souyris, J.C.,
Stoll, B.,
Fruneau, B.,
Rudant, J.P.,
Support Vector Machine for Multifrequency SAR Polarimetric Data
Classification,
GeoRS(47), No. 12, December 2009, pp. 4143-4152.
IEEE DOI
0912
BibRef
Tello, M.[Marivi],
Lopez-Martinez, C.[Carlos],
Mallorqui, J.J.[Jordi J.],
Automatic vessel monitoring with single and multidimensional SAR images
in the wavelet domain,
PandRS(61), No. 3-4, December 2006, pp. 260-278.
Elsevier DOI
0703
Vessel monitoring; Synthetic aperture radar; Polarimetry;
Time-frequency analysis; Wavelet transform
BibRef
Nascimento, A.D.C.,
Frery, A.C.,
Cintra, R.J.,
Detecting Changes in Fully Polarimetric SAR Imagery With Statistical
Information Theory,
GeoRS(57), No. 3, March 2019, pp. 1380-1392.
IEEE DOI
1903
covariance matrices, entropy, Hermitian matrices,
maximum likelihood estimation, radar imaging, radar polarimetry,
Wishart
BibRef
Margarit, G.,
Mallorqui, J.J.,
Rius, J.M.,
Sanz-Marcos, J.,
On the Usage of GRECOSAR, an Orbital Polarimetric SAR Simulator of
Complex Targets, to Vessel Classification Studies,
GeoRS(44), No. 12, December 2006, pp. 3517-3526.
IEEE DOI
0701
BibRef
Margarit, G.,
Mallorqui, J.J.,
Fabregas, X.,
Single-Pass Polarimetric SAR Interferometry for Vessel Classification,
GeoRS(45), No. 11, November 2007, pp. 3494-3502.
IEEE DOI
0709
BibRef
de Grandi, G.D.,
Lee, J.S.,
Schuler, D.L.,
Target Detection and Texture Segmentation in Polarimetric SAR Images
Using a Wavelet Frame: Theoretical Aspects,
GeoRS(45), No. 11, November 2007, pp. 3437-3453.
IEEE DOI
0709
BibRef
Crosby, F.[Frank],
Comparison of directly measured to derived polarization imagery using
an adaptive signature detection algorithm,
IVC(25), No. 11, 1 November 2007, pp. 1759-1766.
Elsevier DOI
0709
Polarization; ATR; Maximum Likelihood Test; CFAR
BibRef
Frery, A.C.[Alejandro C.],
Correia, A.H.[Antonio H.],
Freitas, C.D.C.[Corina Da C.],
Classifying Multifrequency Fully Polarimetric Imagery With Multiple
Sources of Statistical Evidence and Contextual Information,
GeoRS(45), No. 10, October 2007, pp. 3098-3109.
IEEE DOI
0711
BibRef
Anfinsen, S.N.,
Doulgeris, A.P.[Anthony P.],
Eltoft, T.[Torbjørn],
Estimation of the Equivalent Number of Looks in Polarimetric Synthetic
Aperture Radar Imagery,
GeoRS(47), No. 11, November 2009, pp. 3795-3809.
IEEE DOI
0911
BibRef
Doulgeris, A.P.[Anthony P.],
Anfinsen, S.N.,
Eltoft, T.[Torbjørn],
Classification With a Non-Gaussian Model for PolSAR Data,
GeoRS(46), No. 10, October 2008, pp. 2999-3009.
IEEE DOI
0810
BibRef
Cristea, A.[Anca],
Doulgeris, A.P.[Anthony P.],
Eltoft, T.[Torbjørn],
A Noncentral and Non-Gaussian Probability Model for SAR Data,
SCIA17(II: 159-168).
Springer DOI
1706
BibRef
Eltoft, T.[Torbjørn],
Anfinsen, S.N.,
Doulgeris, A.P.[Anthony P.],
A Multitexture Model for Multilook Polarimetric Synthetic Aperture
Radar Data,
GeoRS(52), No. 5, May 2014, pp. 2910-2919.
IEEE DOI
1403
Analytical models
BibRef
Quigley, C.[Cornelius],
Brekke, C.[Camilla],
Eltoft, T.[Torbjørn],
Comparison Between Dielectric Inversion Results From Synthetic
Aperture Radar Co- and Quad-Polarimetric Data via a Polarimetric
Two-Scale Model,
GeoRS(60), 2022, pp. 1-18.
IEEE DOI
2112
Oils, Synthetic aperture radar, Sea surface, Scattering,
Spaceborne radar, Radar polarimetry, Permittivity,
synthetic aperture radar (SAR)
BibRef
McNairn, H.,
Shang, J.,
Jiao, X.,
Champagne, C.,
The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data
to Crop Classification,
GeoRS(47), No. 12, December 2009, pp. 3981-3992.
IEEE DOI
0912
BibRef
Thilak Krishna, T.V.,
Creusere, C.D.[Charles D.],
Voelz, D.G.[David G.],
Passive Polarimetric Imagery-Based Material Classification Robust to
Illumination Source Position and Viewpoint,
IP(20), No. 1, January 2011, pp. 288-292.
IEEE DOI
1101
BibRef
Thilak, V.[Vimal],
Creusere, C.D.[Charles D.],
Voelz, D.G.[David G.],
Passive Polarimetric Imagery Based Material Classification For Remote
Sensing Applications,
Southwest08(153-156).
IEEE DOI
0803
BibRef
Earlier:
Material Classification using Passive Polarimetric Imagery,
ICIP07(IV: 121-124).
IEEE DOI
0709
BibRef
Livanos, G.,
Zervakis, M.,
Giakos, G.C.,
Valluru, K.,
Paturi, S.,
Marotta, S.,
Modelling the characteristics of material distributions in polarimetric
images,
IET-IPR(5), No. 5, 2011, pp. 429-439.
DOI Link
1108
BibRef
Ainsworth, T.L.,
Kelly, J.P.,
Lee, J.S.,
Classification comparisons between dual-pol, compact polarimetric and
quad-pol SAR imagery,
PandRS(64), No. 5, September 2009, pp. 464-471.
Elsevier DOI
0910
Remote sensing; SAR; Classification
BibRef
Sánchez-Lladó, F.J.[Francisco J.],
Pajares, G.[Gonzalo],
López-Martínez, C.[Carlos],
Improving the Wishart Synthetic Aperture Radar image classifications
through Deterministic Simulated Annealing,
PandRS(66), No. 6, November 2011, pp. 845-857.
Elsevier DOI
1112
Synthetic Aperture Radar (SAR); Polarization; Classification;
Deterministic Simulated Annealing; Wishart classifier
BibRef
Pajares, G.[Gonzalo],
Sánchez-Lladó, J.[Javier],
López-Martínez, C.[Carlos],
Fuzzy Cognitive Maps Applied to Synthetic Aperture Radar Image
Classifications,
ACIVS11(103-114).
Springer DOI
1108
BibRef
Kiranyaz, S.[Serkan],
Ince, T.[Turker],
Uhlmann, S.[Stefan],
Gabbouj, M.[Moncef],
Collective Network of Binary Classifier Framework for Polarimetric SAR
Image Classification: An Evolutionary Approach,
SMC-B(42), No. 4, August 2012, pp. 1169-1186.
IEEE DOI
1208
BibRef
Earlier: A3, A1, A4, A2:
Incremental evolution of collective network of binary classifier for
polarimetric SAR image classification,
ICIP11(177-180).
IEEE DOI
1201
BibRef
Uhlmann, S.[Stefan],
Kiranyaz, S.[Serkan],
Gabbouj, M.[Moncef],
Semi-Supervised Learning for Ill-Posed Polarimetric SAR
Classification,
RS(6), No. 6, 2014, pp. 4801-4830.
DOI Link
1407
BibRef
Uhlmann, S.[Stefan],
Kiranyaz, S.[Serkan],
Integrating Color Features in Polarimetric SAR Image Classification,
GeoRS(52), No. 4, April 2014, pp. 2197-2216.
IEEE DOI
1403
data visualisation
BibRef
Ahishali, M.[Mete],
Kiranyaz, S.[Serkan],
Ince, T.[Turker],
Gabbouj, M.[Moncef],
Dual and Single Polarized SAR Image Classification Using Compact
Convolutional Neural Networks,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link
1906
BibRef
Ince, T.[Turker],
Kiranyaz, S.[Serkan],
Gabbouj, M.[Moncef],
Classification of Polarimetric SAR Images Using Evolutionary RBF
Networks,
ICPR10(4324-4327).
IEEE DOI
1008
BibRef
Fuller, D.F.,
Saville, M.A.,
A High-Frequency Multipeak Model for Wide-Angle SAR Imagery,
GeoRS(51), No. 7, 2013, pp. 4279-4291.
IEEE DOI
1307
joint frequency-polarization scatter classification
BibRef
Nashashibi, A.Y.,
Sarabandi, K.,
Al-Zaid, F.A.,
Alhumaidi, S.,
An Empirical Model of Volume Scattering From Dry Sand-Covered Surfaces
at Millimeter-Wave Frequencies,
GeoRS(51), No. 6, 2013, pp. 3673-3682.
IEEE DOI
1307
radar polarimetry
BibRef
Uhlmann, S.[Stefan],
Kiranyaz, S.[Serkan],
Classification of dual- and single polarized SAR images by
incorporating visual features,
PandRS(90), No. 1, 2014, pp. 10-22.
Elsevier DOI
1404
Synthetic aperture radar
BibRef
Shang, F.[Fang],
Hirose, A.,
Quaternion Neural-Network-Based PolSAR Land Classification in
Poincare-Sphere-Parameter Space,
GeoRS(52), No. 9, Sept 2014, pp. 5693-5703.
IEEE DOI
1407
Poincare mapping
BibRef
Shang, F.[Fang],
Hirose, A.,
Averaged Stokes Vector Based Polarimetric SAR Data Interpretation,
GeoRS(53), No. 8, August 2015, pp. 4536-4547.
IEEE DOI
1506
object detection
BibRef
Feng, J.[Jilan],
Cao, Z.[Zongjie],
Pi, Y.M.[Yi-Ming],
Polarimetric Contextual Classification of PolSAR Images Using Sparse
Representation and Superpixels,
RS(6), No. 8, 2014, pp. 7158-7181.
DOI Link
1410
BibRef
Plank, S.[Simon],
Mager, A.[Alexander],
Schoepfer, E.[Elisabeth],
Monitoring of Oil Exploitation Infrastructure by Combining
Unsupervised Pixel-Based Classification of Polarimetric SAR and
Object-Based Image Analysis,
RS(6), No. 12, 2014, pp. 11977-12004.
DOI Link
1412
BibRef
Rogers, G.W.,
Rais, H.,
Cameron, W.L.,
Polarimetric SAR Signature Detection Using the Cameron Decomposition,
GeoRS(52), No. 1, January 2014, pp. 690-700.
IEEE DOI
1402
S-matrix theory
BibRef
Deng, L.[Lei],
Yan, Y.N.[Ya-Nan],
Sun, C.[Chen],
Use of Sub-Aperture Decomposition for Supervised PolSAR
Classification in Urban Area,
RS(7), No. 2, 2015, pp. 1380-1396.
DOI Link
1503
BibRef
Deng, L.[Lei],
Yan, Y.N.[Ya-Nan],
Wang, C.Z.[Cui-Zhen],
Improved POLSAR Image Classification by the Use of Multi-Feature
Combination,
RS(7), No. 4, 2015, pp. 4157-4177.
DOI Link
1505
BibRef
Cheng, J.[Jian],
Ji, Y.Q.[Ya-Qi],
Liu, H.J.[Hai-Jun],
Segmentation-Based PolSAR Image Classification Using Visual Features:
RHLBP and Color Features,
RS(7), No. 5, 2015, pp. 6079-6106.
DOI Link
1506
BibRef
Yang, F.[Fan],
Gao, W.[Wei],
Xu, B.[Bin],
Yang, J.[Jian],
Multi-Frequency Polarimetric SAR Classification Based on Riemannian
Manifold and Simultaneous Sparse Representation,
RS(7), No. 7, 2015, pp. 8469.
DOI Link
1506
BibRef
Liu, C.[Chun],
Yin, J.J.[Jun-Jun],
Yang, J.[Jian],
Gao, W.[Wei],
Classification of Multi-Frequency Polarimetric SAR Images Based on
Multi-Linear Subspace Learning of Tensor Objects,
RS(7), No. 7, 2015, pp. 9253.
DOI Link
1506
BibRef
Fernández-Michelli, J.I.,
Hurtado, M.,
Areta, J.A.,
Muravchik, C.H.,
Unsupervised classification algorithm based on EM method for
polarimetric SAR images,
PandRS(117), No. 1, 2016, pp. 56-65.
Elsevier DOI
1605
SAR images
BibRef
Xu, Q.[Qiao],
Chen, Q.H.[Qi-Hao],
Yang, S.[Shuai],
Liu, X.[Xiuguo],
Superpixel-Based Classification Using K Distribution and Spatial
Context for Polarimetric SAR Images,
RS(8), No. 8, 2016, pp. 619.
DOI Link
1609
BibRef
Hou, B.[Biao],
Wu, Q.[Qian],
Wen, Z.D.[Zai-Dao],
Jiao, L.C.[Li-Cheng],
Robust Semisupervised Classification for PolSAR Image With Noisy
Labels,
GeoRS(55), No. 11, November 2017, pp. 6440-6455.
IEEE DOI
1711
Data models, Noise measurement,
Robustness, Speckle, Synthetic aperture radar,
BibRef
Zhao, J.Q.[Jin-Qi],
Yang, J.[Jie],
Lu, Z.[Zhong],
Li, P.X.[Ping-Xiang],
Liu, W.S.[Wen-Song],
Yang, L.[Le],
A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a
Joint-Classification Classifier Based on a Similarity Measure,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link
1708
BibRef
Liu, W.S.[Wen-Song],
Yang, J.[Jie],
Zhao, J.Q.[Jin-Qi],
Yang, L.[Le],
A Novel Method of Unsupervised Change Detection Using Multi-Temporal
PolSAR Images,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link
1712
BibRef
Zhao, L.[Lei],
Chen, E.[Erxue],
Li, Z.Y.[Zeng-Yuan],
Zhang, W.F.[Wang-Fei],
Gu, X.Z.[Xin-Zhi],
Three-Step Semi-Empirical Radiometric Terrain Correction Approach for
PolSAR Data Applied to Forested Areas,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link
1704
BibRef
Zhao, L.[Lei],
Chen, E.[Erxue],
Li, Z.Y.[Zeng-Yuan],
Fan, Y.X.[Ya-Xiong],
Xu, K.P.[Kun-Peng],
The Improved Three-Step Semi-Empirical Radiometric Terrain Correction
Approach for Supervised Classification of PolSAR Data,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
He, C.[Chu],
Han, G.[Gong],
Feng, D.[Di],
Du, J.[Juan],
Liao, M.S.[Ming-Sheng],
A Sparse Manifold Classification Method Based on a Multi-Dimensional
Descriptive Primitive of Polarimetric SAR Image Time Series,
IJGI(6), No. 4, 2017, pp. xx-yy.
DOI Link
1705
BibRef
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
Bi, H.,
Zhang, B.,
Zhu, X.X.,
Hong, W.,
Sun, J.,
Wu, Y.,
L_1 -Regularization-Based SAR Imaging and CFAR Detection via Complex
Approximated Message Passing,
GeoRS(55), No. 6, June 2017, pp. 3426-3440.
IEEE DOI
1706
Image reconstruction, Imaging, Radar imaging, Radar polarimetry,
Signal processing algorithms, Synthetic aperture radar,
L1 regularization, Lasso,
complex approximated message passing (CAMP),
constant false alarm rate (CFAR) detection, synthetic, aperture,
radar, (SAR)
BibRef
Bi, H.[Hui],
Bi, G.[Guoan],
Zhang, B.C.[Bing-Chen],
Hong, W.[Wen],
Complex-Image-Based Sparse SAR Imaging and its Equivalence,
GeoRS(56), No. 9, September 2018, pp. 5006-5014.
IEEE DOI
1809
Radar polarimetry, Sparse matrices, Imaging,
Synthetic aperture radar, Radar imaging, Image reconstruction,
sparse synthetic aperture radar (SAR) imaging
BibRef
Song, C.[Chen],
Deng, J.R.[Jia-Rui],
Liu, Z.[Zehao],
Wang, B.N.[Bing-Nan],
Wu, Y.R.[Yi-Rong],
Bi, H.[Hui],
Complex-Valued Sparse SAR-Image-Based Target Detection and
Classification,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Bi, H.[Hui],
Zhang, B.C.[Bing-Chen],
Zhu, X.X.,
Jiang, C.,
Hong, W.,
Extended Chirp Scaling-Baseband Azimuth Scaling-Based Azimuth: Range
Decouple L_1 Regularization for TOPS SAR Imaging via CAMP,
GeoRS(55), No. 7, July 2017, pp. 3748-3763.
IEEE DOI
1706
Azimuth, Chirp, Image reconstruction, Imaging, Radar polarimetry,
Signal processing algorithms, Synthetic aperture radar,
L1 regularization, azimuth-range decouple,
baseband azimuth scaling (BAS),
complex approximated message passing (CAMP),
extended chirp scaling (ECS), synthetic aperture radar (SAR),
terrain, observation, by, progressive, scans, (TOPS)
BibRef
Yang, H.Y.[Hai-Yi],
Cao, Z.J.[Zong-Jie],
Cui, Z.Y.[Zong-Yong],
Pi, Y.M.[Yi-Ming],
Saliency detection of targets in polarimetric SAR images based on
globally weighted perturbation filters,
PandRS(147), 2019, pp. 65-79.
Elsevier DOI
1901
Saliency detection, Polarimetric synthetic aperture radar,
Geometrical perturbation filter, Sparse spatial correlation
BibRef
Wang, L.[Li],
Bai, X.[Xueru],
Gong, C.[Chen],
Zhou, F.[Feng],
Hybrid Inference Network for Few-Shot SAR Automatic Target
Recognition,
GeoRS(59), No. 11, November 2021, pp. 9257-9269.
IEEE DOI
2111
Training, Synthetic aperture radar, Target recognition,
Task analysis, Radar polarimetry, Manifolds, Feature extraction,
synthetic aperture radar (SAR)
BibRef
Yang, M.J.[Min-Jia],
Bai, X.[Xueru],
Wang, L.[Li],
Zhou, F.[Feng],
HENC: Hierarchical Embedding Network With Center Calibration for
Few-Shot Fine-Grained SAR Target Classification,
IP(32), 2023, pp. 3324-3337.
IEEE DOI
2307
Feature extraction, Training, Task analysis, Radar polarimetry,
Calibration, Visualization, Synthetic aperture radar,
fine-grained classification
BibRef
Bai, X.R.[Xue-Ru],
Xue, R.H.[Rui-Hang],
Wang, L.[Li],
Zhou, F.[Feng],
Sequence SAR Image Classification Based on Bidirectional
Convolution-Recurrent Network,
GeoRS(57), No. 11, November 2019, pp. 9223-9235.
IEEE DOI
1911
Feature extraction, Synthetic aperture radar, Radar polarimetry,
Convolution, Periodic structures, Image sequences, Kernel,
target classification
BibRef
Fan, W.W.[Wei-Wei],
Zhou, F.[Feng],
Tao, M.L.[Ming-Liang],
Bai, X.[Xueru],
Rong, P.[Pengshuai],
Yang, S.[Shuang],
Tian, T.[Tian],
Interference Mitigation for Synthetic Aperture Radar Based on Deep
Residual Network,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link
1908
BibRef
Shi, X.R.[Xiao-Ran],
Zhou, F.[Feng],
Yang, S.[Shuang],
Zhang, Z.J.[Zi-Jing],
Su, T.[Tao],
Automatic Target Recognition for Synthetic Aperture Radar Images
Based on Super-Resolution Generative Adversarial Network and Deep
Convolutional Neural Network,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link
1902
BibRef
Zhou, Z.[Zhi],
Cao, Z.J.[Zong-Jie],
Pi, Y.M.[Yi-Ming],
Subdictionary-Based Joint Sparse Representation for SAR Target
Recognition Using Multilevel Reconstruction,
GeoRS(57), No. 9, September 2019, pp. 6877-6887.
IEEE DOI
1909
Scattering, Image reconstruction, Radar polarimetry,
Synthetic aperture radar, Feature extraction, Solid modeling,
subdictionary
BibRef
An, Q.,
Pan, Z.,
Liu, L.,
You, H.,
DRBox-v2: An Improved Detector With Rotatable Boxes for Target
Detection in SAR Images,
GeoRS(57), No. 11, November 2019, pp. 8333-8349.
IEEE DOI
1911
Object detection, Feature extraction, Detectors, Proposals,
Radar polarimetry, Remote sensing, Encoding,
target detection
BibRef
Zhang, L.,
Zhang, Y.,
Yin, H.,
He, S.,
Zhu, G.,
A Fast SAR Target Indexing Method Based on Geometric Models,
GeoRS(57), No. 12, December 2019, pp. 10226-10240.
IEEE DOI
1912
Scattering, Indexing, Synthetic aperture radar, Feature extraction,
Radar polarimetry, Computational modeling, Target recognition,
synthetic aperture radar (SAR)
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
Cao, C.,
Cao, Z.,
Cui, Z.,
LDGAN: A Synthetic Aperture Radar Image Generation Method for
Automatic Target Recognition,
GeoRS(58), No. 5, May 2020, pp. 3495-3508.
IEEE DOI
2005
Radar polarimetry, Image recognition,
Synthetic aperture radar, Target recognition, Training,
BibRef
Du, L.,
Wang, Y.,
Xie, W.,
Wang, Z.,
Chen, J.,
A Semisupervised Infinite Latent Dirichlet Allocation Model for
Target Discrimination in SAR Images With Complex Scenes,
GeoRS(58), No. 1, January 2020, pp. 666-679.
IEEE DOI
2001
Semantics, Synthetic aperture radar, Radar polarimetry, Training,
Feature extraction, Support vector machines, Clutter,
target discrimination
BibRef
Liang, W.,
Wu, Y.,
Li, M.,
Cao, Y.,
High-Resolution SAR Image Classification Using Context-Aware Encoder
Network and Hybrid Conditional Random Field Model,
GeoRS(58), No. 8, August 2020, pp. 5317-5335.
IEEE DOI
2007
Radar polarimetry, Synthetic aperture radar, Training,
Context modeling, Semantics, Labeling, Computational modeling,
pixel-wise image classification
BibRef
Zhang, J.,
Xing, M.,
Xie, Y.,
FEC: A Feature Fusion Framework for SAR Target Recognition Based on
Electromagnetic Scattering Features and Deep CNN Features,
GeoRS(59), No. 3, March 2021, pp. 2174-2187.
IEEE DOI
2103
Feature extraction, Target recognition, Synthetic aperture radar,
Image recognition, Electromagnetic scattering, Radar polarimetry,
target recognition
BibRef
Dong, G.,
Liu, H.,
Global Receptive-Based Neural Network for Target Recognition in SAR
Images,
Cyber(51), No. 4, April 2021, pp. 1954-1967.
IEEE DOI
2103
Target recognition, Scattering, Feature extraction,
Radar polarimetry, Training, Task analysis, Neural networks,
unconstrained environment
BibRef
Wang, C.[Chen],
Shi, J.[Jun],
Zhou, Y.Y.[Yuan-Yuan],
Yang, X.Q.[Xia-Qing],
Zhou, Z.[Zenan],
Wei, S.J.[Shun-Jun],
Zhang, X.L.[Xiao-Ling],
Semisupervised Learning-Based SAR ATR via Self-Consistent
Augmentation,
GeoRS(59), No. 6, June 2021, pp. 4862-4873.
IEEE DOI
2106
Training, Synthetic aperture radar, Semisupervised learning,
Supervised learning, Training data, Radar polarimetry,
synthetic aperture radar (SAR)
BibRef
Wang, S.L.,
Xu, Z.,
Dong, W.,
Wang, G.,
A Scheme of Polarimetric Superresolution for Multitarget Detection
and Localization,
SPLetters(28), 2021, pp. 439-443.
IEEE DOI
2103
Superresolution, Estimation, Radar, Array signal processing,
Signal to noise ratio, Parametric statistics, Indexes,
true target extractor
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],
Combination of Hyperspectral and Quad-Polarization SAR Images to
Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link
2212
BibRef
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,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Qian, X.X.[Xiao-Xue],
Liu, F.[Fang],
Jiao, L.C.[Li-Cheng],
Zhang, X.R.[Xiang-Rong],
Chen, P.[Puhua],
Li, L.L.[Ling-Ling],
Gu, J.[Jing],
Cui, Y.H.[Yuan-Hao],
A Hybrid Network With Structural Constraints for SAR Image Scene
Classification,
GeoRS(60), 2022, pp. 1-17.
IEEE DOI
2112
Radar polarimetry, Bayes methods, Data models,
Synthetic aperture radar, Feature extraction, Neural networks,
synthetic aperture radar (SAR) image scene classification
BibRef
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,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
BibRef
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,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
BibRef
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],
Feature Extraction for Moving Targets Based on the Statistical
Characteristics of Echo Amplitude with the L-Band Fully Polarimetric
Radar,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Fang, Z.[Zheng],
Zhang, G.[Gong],
Dai, Q.J.[Qi-Jun],
Xue, B.[Biao],
Wang, P.[Peng],
Hybrid Attention-Based Encoder-Decoder Fully Convolutional
Network for PolSAR Image Classification,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Li, Z.C.[Ze-Chen],
Li, H.C.[Heng-Chao],
Gao, G.[Gui],
Hua, Z.X.[Ze-Xi],
Zhang, F.[Fan],
Hong, W.[Wen],
Unsupervised classification of polarimetric SAR images via SVGp0MM
with extended variational inference,
PandRS(196), 2023, pp. 256-269.
Elsevier DOI
2302
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],
A Polarimetric Scattering Characteristics-Guided Adversarial Learning
Approach for Unsupervised PolSAR Image Classification,
RS(15), No. 7, 2023, pp. 1782.
DOI Link
2304
BibRef
Wang, W.K.[Wen-Ke],
Wang, J.L.[Jian-Long],
Lu, B.[Bibo],
Liu, B.[Boyuan],
Zhang, Y.[Yake],
Wang, C.Y.[Chun-Yang],
MCPT: Mixed Convolutional Parallel Transformer for Polarimetric SAR
Image Classification,
RS(15), No. 11, 2023, pp. 2936.
DOI Link
2306
BibRef
Han, W.T.[Wen-Tao],
Fu, H.Q.[Hai-Qiang],
Zhu, J.J.[Jian-Jun],
Zhang, S.[Shurong],
Xie, Q.H.[Qing-Hua],
Hu, J.[Jun],
A polarimetric projection-based scattering characteristics extraction
tool and its application to PolSAR image classification,
PandRS(202), 2023, pp. 314-333.
Elsevier DOI
2308
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],
Polarimetric Synthetic Aperture Radar Image Classification Based on
Double-Channel Convolution Network and Edge-Preserving Markov Random
Field,
RS(15), No. 23, 2023, pp. 5458.
DOI Link
2312
BibRef
Wang, Z.[Zehua],
Wang, Z.Z.[Ze-Zhong],
Qiu, X.L.[Xiao-Lan],
Zhang, Z.[Zhe],
Global Polarimetric Synthetic Aperture Radar Image Segmentation with
Data Augmentation and Hybrid Architecture Model,
RS(16), No. 2, 2024, pp. 380.
DOI Link
2402
BibRef
Wang, T.T.[Ting-Ting],
Suo, Z.Y.[Zhi-Yong],
Ti, J.J.[Jing-Jing],
Yan, B.[Boya],
Xiang, H.L.[Hong-Li],
Xi, J.[Jiabao],
A Unitary Transformation Extension of PolSAR Four-Component Target
Decomposition,
RS(16), No. 6, 2024, pp. 1067.
DOI Link
2403
BibRef
Ren, S.J.[Shi-Jie],
Zhou, F.[Feng],
Bruzzone, L.[Lorenzo],
Transfer-Aware Graph U-Net with Cross-Level Interactions for PolSAR
Image Semantic Segmentation,
RS(16), No. 8, 2024, pp. 1428.
DOI Link
2405
BibRef
Zhang, S.Y.[Shuai-Ying],
Cui, L.Z.[Li-Zhen],
Dong, Z.[Zhen],
An, W.T.[Wen-Tao],
A Deep Learning Classification Scheme for PolSAR Image Based on
Polarimetric Features,
RS(16), No. 10, 2024, pp. 1676.
DOI Link
2405
BibRef
Zhang, S.Y.[Shuai-Ying],
Cui, L.Z.[Li-Zhen],
Zhang, Y.[Yue],
Xia, T.[Tian],
Dong, Z.[Zhen],
An, W.T.[Wen-Tao],
Research on Input Schemes for Polarimetric SAR Classification Using
Deep Learning,
RS(16), No. 11, 2024, pp. 1826.
DOI Link
2406
BibRef
Löw, J.[Johannes],
Hill, S.[Steven],
Otte, I.[Insa],
Thiel, M.[Michael],
Ullmann, T.[Tobias],
Conrad, C.[Christopher],
How Phenology Shapes Crop-Specific Sentinel-1 PolSAR Features and
InSAR Coherence across Multiple Years and Orbits,
RS(16), No. 15, 2024, pp. 2791.
DOI Link
2408
BibRef
Wang, L.[Lei],
Peng, L.[Lingmu],
Gui, R.[Rong],
Hong, H.Y.[Han-Yu],
Zhu, S.H.[Sheng-Hui],
Unsupervised PolSAR Image Classification Based on Superpixel
Pseudo-Labels and a Similarity-Matching Network,
RS(16), No. 21, 2024, pp. 4119.
DOI Link
2411
BibRef
Wang, N.W.[Ning-Wei],
Jin, W.Q.[Wei-Qiang],
Bi, H.X.[Hai-Xia],
Xu, C.[Chen],
Gao, J.H.[Jing-Huai],
A Survey on Deep Learning for Few-Shot PolSAR Image Classification,
RS(16), No. 24, 2024, pp. 4632.
DOI Link
2501
BibRef
Gao, X.Z.[Xi-Zhan],
Wei, K.[Kang],
Niu, S.[Sijie],
Zhao, H.[Hui],
Dong, J.W.[Ji-Wen],
EDLRDPL_Net: A New Deep Dictionary Learning Network for SAR Image
Classification,
SPLetters(31), 2024, pp. 1459-1463.
IEEE DOI
2406
Dictionaries, Entropy, Machine learning, Radar polarimetry,
Classification algorithms, Optimization, Image reconstruction,
SAR image classification
BibRef
Wang, R.Q.[Rui-Qiu],
Su, T.[Tao],
Xu, D.[Dan],
Chen, J.[Jianlai],
Liang, Y.[Yuan],
MIGA-Net: Multi-View Image Information Learning Based on Graph
Attention Network for SAR Target Recognition,
CirSysVideo(34), No. 11, November 2024, pp. 10779-10792.
IEEE DOI
2412
Feature extraction, Azimuth, Synthetic aperture radar, Training,
Data mining, Radar polarimetry, Image sequences,
synthetic aperture radar automatic target recognition (SAR ATR)
BibRef
Geng, J.[Jie],
Ma, W.C.[Wei-Chen],
Jiang, W.[Wen],
Causal Intervention and Parameter-Free Reasoning for Few-Shot SAR
Target Recognition,
CirSysVideo(34), No. 12, December 2024, pp. 12702-12714.
IEEE DOI
2501
Target recognition, Synthetic aperture radar, Transfer learning,
Feature extraction, Training, Radar polarimetry, Metalearning,
optimal transport
BibRef
Guo, W.L.[Wei-Long],
Liv, S.Y.[Sheng-Yang],
Yang, J.[Jian],
Scattering Prompt Tuning: A Fine-tuned Foundation Model for SAR
Object Recognition,
PBVS24(3056-3065)
IEEE DOI
2410
Training, Scattering, Imaging, Semisupervised learning,
Radar imaging, Radar polarimetry, SAR, Fine-tuned
BibRef
Tai, T.J.[Tsen-Jung],
Toda, M.[Masato],
Adapting Intra-Class Variations for SAR Image Classification,
ICIP21(2653-2657)
IEEE DOI
2201
Image recognition, Target recognition, Annotations,
Face recognition, Radar polarimetry, Land vehicles,
Intra-class variance
BibRef
Huang, X.Y.[Xia-Yuan],
Nie, X.L.[Xiang-Li],
Qiao, H.[Hong],
Zhang, B.[Bo],
Supervised Polsar Image Classification by Combining Multiple Features,
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
Rouabah, S.,
Ouarzeddine, M.,
Azmedroub, B.,
Polarimetric SAR Data GMM Classification Based On Improved Freeman
Incoherent Decomposition,
ISPRS16(B7: 341-345).
DOI Link
1610
BibRef
Liu, F.[Fang],
Shi, J.F.[Jun-Fei],
Jiao, L.C.[Li-Cheng],
Liu, H.Y.[Hong-Ying],
Yang, S.Y.[Shu-Yuan],
Wu, J.[Jie],
Hao, H.X.[Hong-Xia],
Yuan, J.L.[Jia-Ling],
Hierarchical semantic model and scattering mechanism based PolSAR
image classification,
PR(59), No. 1, 2016, pp. 325-342.
Elsevier DOI
1609
Hierarchical semantic model
BibRef
Wang, X.J.[Xiao-Jun],
Li, H.[Hao],
Wu, Y.H.[Yong-Hui],
Yan, S.S.[Shu-Sheng],
Li, L.H.[Lian-Hua],
Parameters analysis for polarimetric SAR Based on classification
accuracy,
IASP10(268-271).
IEEE DOI
1004
BibRef
Cai, A.[Aimin],
Shao, Y.[Yun],
Gong, H.[Huaze],
Parameters extraction of crop based on PolSAR Data,
IASP10(12-15).
IEEE DOI
1004
BibRef
Hansch, R.[Ronny],
Hellwich, O.[Olaf],
Classification of Polarimetric SAR Data by Complex Valued Neural
Networks,
HighRes09(xx-yy).
PDF File.
0906
BibRef
Belhadj, Z.,
Benazza, A.,
Hidoussi, N.,
Classification of radar images in polarimetric remote sensing,
ICIP98(I: 574-577).
IEEE DOI
9810
BibRef
Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Motion Compensation for Radar .