22.2.11 Radar for Land Cover, SAR for Land Cover, Remote Sensing

Chapter Contents (Back)
Classification. SAR. Radar. Remote Sensing.
See also Sentinel-1, -2, -3 for Land Cover, Remote Sensing.

Soares, J.V., Renno, C.D., Formaggio, A.R., Yanasse, C.D.C.F., Frery, A.C.,
An Investigation of the Selection of Texture Features for Crop Discrimination Using SAR Imagery,
RSE(59), No. 2, February 1997, pp. 234-247. 9704

Mandal, D., Ratha, D., Bhattacharya, A., Kumar, V., McNairn, H., Rao, Y.S., Frery, A.C.,
A Radar Vegetation Index for Crop Monitoring Using Compact Polarimetric SAR Data,
GeoRS(58), No. 9, September 2020, pp. 6321-6335.
Agriculture, Vegetation mapping, Synthetic aperture radar, Scattering, Indexes, Monitoring, Compact-pol, crop, vegetation index BibRef

Karjalainen, M.[Mika], Kaartinen, H.[Harri], Hyyppä, J.[Juha],
Agricultural Monitoring Using Envisat Alternating Polarization SAR Images,
PhEngRS(74), No. 1, January 2008, pp. 117-128
WWW Link. 0803
Satellite images will improve yield estimation in the future because they can provide objective information about crop growth over large areas; in this context SAR images are extremely useful due to their high revisit imaging capability. BibRef

Richards, J.A.[John A.],
Remote Sensing with Imaging Radar,
Springer2009, ISBN: 978-3-642-02019-3
WWW Link. Radar. SAR. Buy this book: Remote Sensing with Imaging Radar (Signals and Communication Technology) 0911

Waske, B.[Bjorn], Braun, M.[Matthias],
Classifier ensembles for land cover mapping using multitemporal SAR imagery,
PandRS(64), No. 5, September 2009, pp. 450-457.
Elsevier DOI 0910
Decision tree; Random forests; Boosting; Multitemporal SAR data; Land cover classification BibRef

Bargiel, D., Herrmann, S.,
Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data,
RS(3), No. 5, May 2011, pp. 859-877.
DOI Link 1203

Laurila, H., Karjalainen, M., Hyyppä, J., Kleemola, J.,
Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I),
RS(2), No. 1, January 2010, pp. 76-114.
DOI Link 1203

Esch, T., Schenk, A., Ullmann, T., Thiel, M., Roth, A., Dech, S.,
Characterization of Land Cover Types in TerraSAR-X Images by Combined Analysis of Speckle Statistics and Intensity Information,
GeoRS(49), No. 6, June 2011, pp. 1911-1925.

See also Delineation of Urban Footprints From TerraSAR-X Data by Analyzing Speckle Characteristics and Intensity Information. BibRef

Longepe, N., Rakwatin, P., Isoguchi, O., Shimada, M., Uryu, Y., Yulianto, K.,
Assessment of ALOS PALSAR 50 m Orthorectified FBD Data for Regional Land Cover Classification by Support Vector Machines,
GeoRS(49), No. 6, June 2011, pp. 2135-2150.

Bagan, H., Kinoshita, T., Yamagata, Y.,
Combination of AVNIR-2, PALSAR, and Polarimetric Parameters for Land Cover Classification,
GeoRS(50), No. 4, April 2012, pp. 1318-1328.

Li, G.Y.[Gui-Ying], Lu, D.S.[Deng-Sheng], Moran, E.[Emilio], Dutra, L.[Luciano], Batistella, M.[Mateus],
A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region,
PandRS(70), No. 1, June 2012, pp. 26-38.
Elsevier DOI 1206
ALOS PALSAR; RADARSAT; Texture; Land-cover classification; Amazon BibRef

Loosvelt, L., Peters, J., Skriver, H., de Baets, B., Verhoest, N.E.C.,
Impact of Reducing Polarimetric SAR Input on the Uncertainty of Crop Classifications Based on the Random Forests Algorithm,
GeoRS(50), No. 10, October 2012, pp. 4185-4200.

Skriver, H.,
Crop Classification by Multitemporal C- and L-Band Single- and Dual-Polarization and Fully Polarimetric SAR,
GeoRS(50), No. 6, June 2012, pp. 2138-2149.

Chen, Q., Kuang, G., Li, J., Sui, L., Li, D.,
Unsupervised Land Cover/Land Use Classification Using PolSAR Imagery Based on Scattering Similarity,
GeoRS(51), No. 3, March 2013, pp. 1817-1825.

Shi, L.[Lei], Zhang, L.F.[Le-Fei], Zhao, L.L.[Ling-Li], Yang, J.[Jie], Li, P.X.[Ping-Xiang], Zhang, L.P.[Liang-Pei],
The potential of linear discriminative Laplacian eigenmaps dimensionality reduction in polarimetric SAR classification for agricultural areas,
PandRS(86), No. 1, 2013, pp. 124-135.
Elsevier DOI 1312
Polarimetric synthetic aperture radar BibRef

Mishra, P., Singh, D.,
A Statistical-Measure-Based Adaptive Land Cover Classification Algorithm by Efficient Utilization of Polarimetric SAR Observables,
GeoRS(52), No. 5, May 2014, pp. 2889-2900.
Backscatter BibRef

Arnaubec, A., Roueff, A., Dubois-Fernandez, P.C., Refregier, P.,
Vegetation Height Estimation Precision With Compact PolInSAR and Homogeneous Random Volume Over Ground Model,
GeoRS(52), No. 3, March 2014, pp. 1879-1891.
radar interferometry BibRef

Cable, J.W.[Jeffrey W.], Kovacs, J.M.[John M.], Jiao, X.F.[Xian-Feng], Shang, J.L.[Jia-Li],
Agricultural Monitoring in Northeastern Ontario, Canada, Using Multi-Temporal Polarimetric RADARSAT-2 Data,
RS(6), No. 3, 2014, pp. 2343-2371.
DOI Link 1404

Jiao, X.F.[Xian-Feng], Kovacs, J.M.[John M.], Shang, J.L.[Jia-Li], McNairn, H.[Heather], Walters, D.[Dan], Ma, B.[Baoluo], Geng, X.Y.[Xiao-Yuan],
Object-Oriented Crop Mapping and Monitoring Using Multi-Temporal Polarimetric RADARSAT-2 Data,
PandRS(96), No. 1, 2014, pp. 38-46.
Elsevier DOI 1410
See also Multiyear Crop Monitoring Using Polarimetric RADARSAT-2 Data. BibRef

Cable, J.W.[Jeffrey W.], Kovacs, J.M.[John M.], Shang, J.L.[Jia-Li], Jiao, X.F.[Xian-Feng],
Multi-Temporal Polarimetric RADARSAT-2 for Land Cover Monitoring in Northeastern Ontario, Canada,
RS(6), No. 3, 2014, pp. 2372-2392.
DOI Link 1404

Gao, W.[Wei], Yang, J.[Jian], Ma, W.T.[Wen-Ting],
Land Cover Classification for Polarimetric SAR Images Based on Mixture Models,
RS(6), No. 5, 2014, pp. 3770-3790.
DOI Link 1407

Wang, H.M.[Hong-Miao], Xing, C.[Cheng], Yin, J.J.[Jun-Jun], Yang, J.[Jian],
Land Cover Classification for Polarimetric SAR Images Based on Vision Transformer,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209

Uslu, E.[Erkan], Albayrak, S.[Songul],
Synthetic Aperture Radar Image Clustering with Curvelet Subband Gauss Distribution Parameters,
RS(6), No. 6, 2014, pp. 5497-5519.
DOI Link 1407

Forkuor, G.[Gerald], Conrad, C.[Christopher], Thiel, M.[Michael], Ullmann, T.[Tobias], Zoungrana, E.[Evence],
Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa,
RS(6), No. 7, 2014, pp. 6472-6499.
DOI Link 1408

Forkuor, G.[Gerald], Conrad, C.[Christopher], Thiel, M.[Michael], Zoungrana, B.J.B.[Benewinde J.B.], Tondoh, J.E.[Jérôme E.],
Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708

Cremon, É.H.[Édipo Henrique], de Fátima Rossetti, D.[Dilce], Zani, H.[Hiran],
Classification of Vegetation over a Residual Megafan Landform in the Amazonian Lowland Based on Optical and SAR Imagery,
RS(6), No. 11, 2014, pp. 10931-10946.
DOI Link 1412

Alonso-Gonzalez, A., Lopez-Martinez, C., Salembier, P.,
PolSAR Time Series Processing With Binary Partition Trees,
GeoRS(52), No. 6, June 2014, pp. 3553-3567.
Covariance matrices BibRef

Alonso-González, A., López-Martínez, C., Papathanassiou, K.P., Hajnsek, I.,
Polarimetric SAR Time Series Change Analysis Over Agricultural Areas,
GeoRS(58), No. 10, October 2020, pp. 7317-7330.
Synthetic aperture radar, Scattering, Agriculture, Spaceborne radar, Monitoring, Time series analysis, Agriculture, time series BibRef

Alonso-Gonzalez, A., Jagdhuber, T., Hajnsek, I.,
Agricultural monitoring with polarimetric SAR time series,
radar imaging BibRef

Alonso-Gonzalez, A., Lopez-Martinez, C., Hajnsek, I.,
Processing polarimetric SAR time series over urban areas with binary partition trees,
geophysical techniques BibRef

Alonso-Gonzalez, A., Valero, S., Chanussot, J., Lopez-Martinez, C., Salembier, P.,
Processing Multidimensional SAR and Hyperspectral Images With Binary Partition Tree,
PIEEE(100), No. 3, March 2013, pp. 723-747.

Valero, S.[Silvia], Salembier, P.[Philippe], Chanussot, J.[Jocelyn],
Hyperspectral Image Representation and Processing With Binary Partition Trees,
IP(22), No. 4, April 2013, pp. 1430-1443.
Hyperspectral image segmentation using Binary Partition Trees,
Comparison of merging orders and pruning strategies for Binary Partition Tree in hyperspectral data,

See also Binary Partition Tree as an Efficient Representation for Image Processing, Segmentation, and Information Retrieval.
See also Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation. BibRef

Valero, S.[Silvia], Salembier, P.[Philippe], Chanussot, J.[Jocelyn],
Object recognition in hyperspectral images using Binary Partition Tree representation,
PRL(56), No. 1, 2015, pp. 45-51.
Elsevier DOI 1503
Object based image analysis BibRef

Kweon, S.K.[Soon-Koo], Oh, Y.[Yisok],
A Modified Water-Cloud Model With Leaf Angle Parameters for Microwave Backscattering From Agricultural Fields,
GeoRS(53), No. 5, May 2015, pp. 2802-2809.
geophysical techniques scattering model for radar backscatters of agricultural fields. BibRef

Masjedi, A., Zoej, M.J.V.[M.J. Valadan], Maghsoudi, Y.,
Classification of Polarimetric SAR Images Based on Modeling Contextual Information and Using Texture Features,
GeoRS(54), No. 2, February 2016, pp. 932-943.
Accuracy BibRef

Dargahi, A., Maghsoudi, Y., Abkar, A.A.,
Supervised Classification of Polarimetric SAR Imagery Using Temporal and Contextual Information,
DOI Link 1311

Anghel, A.[Andrei], Vasile, G.[Gabriel], Boudon, R.[Rémy], d'Urso, G.[Guy], Girard, A.[Alexandre], Boldo, D.[Didier], Bost, V.[Véronique],
Combining spaceborne SAR images with 3D point clouds for infrastructure monitoring applications,
PandRS(111), No. 1, 2016, pp. 45-61.
Elsevier DOI 1601
Synthetic aperture radar (SAR) BibRef

Hütt, C.[Christoph], Koppe, W.[Wolfgang], Miao, Y.X.[Yu-Xin], Bareth, G.[Georg],
Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images,
RS(8), No. 8, 2016, pp. 684.
DOI Link 1609

Mascolo, L.[Lucio], Lopez-Sanchez, J.M.[Juan M.], Vicente-Guijalba, F., Nunziata, F., Migliaccio, M., Mazzarella, G.,
A Complete Procedure for Crop Phenology Estimation With PolSAR Data Based on the Complex Wishart Classifier,
GeoRS(54), No. 11, November 2016, pp. 6505-6515.
Agriculture BibRef

Mascolo, L.[Lucio], Martinez-Marin, T.[Tomas], Lopez-Sanchez, J.M.[Juan M.],
Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112

Duguay, Y.[Yannick], Bernier, M.[Monique], Lévesque, E.[Esther], Domine, F.[Florent],
Land Cover Classification in SubArctic Regions Using Fully Polarimetric RADARSAT-2 Data,
RS(8), No. 9, 2016, pp. 697.
DOI Link 1610

Pepe, A.[Antonio], Bonano, M.[Manuela], Zhao, Q.[Qing], Yang, T.L.[Tian-Liang], Wang, H.[Hanmei],
The Use of C-/X-Band Time-Gapped SAR Data and Geotechnical Models for the Study of Shanghai's Ocean-Reclaimed Lands through the SBAS-DInSAR Technique,
RS(8), No. 11, 2016, pp. 911.
DOI Link 1612

Jiang, M.[Mi], Yong, B.[Bin], Tian, X.[Xin], Malhotra, R.[Rakesh], Hu, R.[Rui], Li, Z.W.[Zhi-Wei], Yu, Z.B.[Zhong-Bo], Zhang, X.X.[Xin-Xin],
The potential of more accurate InSAR covariance matrix estimation for land cover mapping,
PandRS(126), No. 1, 2017, pp. 120-128.
Elsevier DOI 1704
Urban remote sensing BibRef

Tian, X.[Xin], Jiang, M.[Mi], Xiao, R.[Ruya], Malhotra, R.[Rakesh],
Bias Removal for Goldstein Filtering Power Using a Second Kind Statistical Coherence Estimator,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Capodici, F.[Fulvio], Maltese, A.[Antonino], Ciraolo, G.[Giuseppe], d'Urso, G.[Guido], La Loggia, G.[Goffredo],
Power Sensitivity Analysis of Multi-Frequency, Multi-Polarized, Multi-Temporal SAR Data for Soil-Vegetation System Variables Characterization,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708

Kenduiywo, B.K., Bargiel, D., Soergel, U.,
Higher Order Dynamic Conditional Random Fields Ensemble for Crop Type Classification in Radar Images,
GeoRS(55), No. 8, August 2017, pp. 4638-4654.
Agriculture, Backscatter, Context, Data models, Radar imaging, Sensors, Classifier ensemble, conditional random fields (CRFs), dynamic CRFs (DCRFs), phenology, radar, spatial-temporal/multitemporal, classification BibRef

Qi, Z.X.[Zhi-Xin], Yeh, A.G.O.[Anthony Gar-On], Li, X.[Xia],
A crop phenology knowledge-based approach for monthly monitoring of construction land expansion using polarimetric synthetic aperture radar imagery,
PandRS(133), No. Supplement C, 2017, pp. 1-17.
Elsevier DOI 1711
Knowledge-based approach, Crop phenology, Construction land expansion, Monthly detection, Polarimetric SAR BibRef

Li, D., Yang, C., Du, Y.,
Efficient Method for Scattering From Cylindrical Components of Vegetation and Its Potential Application to the Determination of Effective Permittivity,
GeoRS(55), No. 11, November 2017, pp. 6120-6127.
Dielectrics, Manganese, Nonhomogeneous media, Permittivity, Scattering, Vegetation, Vegetation mapping, Cylindric component, T-matrix, effective permittivity, orientation, distribution BibRef

Hagensieker, R.[Ron], Waske, B.[Björn],
Evaluation of Multi-Frequency SAR Images for Tropical Land Cover Mapping,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804

Guarnieri, A.M.[A. Monti], Leanza, A., Recchia, A., Tebaldini, S., Venuti, G.,
Atmospheric Phase Screen in GEO-SAR: Estimation and Compensation,
GeoRS(56), No. 3, March 2018, pp. 1668-1679.
atmospheric techniques, atmospheric turbulence, remote sensing by radar, spaceborne radar, synthetic aperture radar (SAR) BibRef

Kim, H., Hirose, A.,
Unsupervised Fine Land Classification Using Quaternion Autoencoder-Based Polarization Feature Extraction and Self-Organizing Mapping,
GeoRS(56), No. 3, March 2018, pp. 1839-1851.
feature extraction, geophysical image processing, image classification, radar imaging, radar polarimetry, unsupervised classification BibRef

Kim, H., Hirose, A.,
Unsupervised Hierarchical Land Classification Using Self-Organizing Feature Codebook for Decimeter-Resolution PolSAR,
GeoRS(57), No. 4, April 2019, pp. 1894-1905.
airborne radar, forestry, geophysical image processing, image classification, land cover, radar imaging, radar polarimetry, unsupervised land classification BibRef

Ohki, M., Shimada, M.,
Large-Area Land Use and Land Cover Classification With Quad, Compact, and Dual Polarization SAR Data by PALSAR-2,
GeoRS(56), No. 9, September 2018, pp. 5550-5557.
Synthetic aperture radar, Feature extraction, Polarimetry, Satellites, Polarization, Data mining, Image classification, synthetic aperture radar (SAR) BibRef

Vaduva, C.[Corina], Dani?or, C.[Cosmin], Datcu, M.[Mihai],
Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
SAR not dependend on sunlight and weather. BibRef

Giordano, S., Mercier, G., Rudant, J.,
Unmixing Polarimetric Radar Images Based on Land Cover Type Identified by Higher Resolution Optical Data Before Target Decomposition: Application to Forest and Bare Soil,
GeoRS(56), No. 10, October 2018, pp. 5850-5862.
Radar polarimetry, Radar imaging, Laser radar, Optical imaging, Optical scattering, Cooperative fusion, low-level fusion, unmixing BibRef

Molijn, R.A.[Ramses A.], Iannini, L.[Lorenzo], Dekker, P.L.[Paco López], Magalhães, P.S.G.[Paulo S.G.], Hanssen, R.F.[Ramon F.],
Vegetation Characterization through the Use of Precipitation-Affected SAR Signals,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
And: Erratum: RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Chang, J.G., Shoshany, M., Oh, Y.,
Polarimetric Radar Vegetation Index for Biomass Estimation in Desert Fringe Ecosystems,
GeoRS(56), No. 12, December 2018, pp. 7102-7108.
Biomass, Radar, Vegetation mapping, L-band, Correlation, Indexes, Biological system modeling, ALOS-PALSAR, degree of polarization, shrublands BibRef

d'Hondt, O.[Olivier], Hänsch, R.[Ronny], Wagener, N.[Nicolas], Hellwich, O.[Olaf],
Exploiting SAR Tomography for Supervised Land-Cover Classification,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Szigarski, C.[Christoph], Jagdhuber, T.[Thomas], Baur, M.[Martin], Thiel, C.[Christian], Parrens, M.[Marie], Wigneron, J.P.[Jean-Pierre], Piles, M.[Maria], Entekhabi, D.[Dara],
Analysis of the Radar Vegetation Index and Potential Improvements,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Li, X.M.[Xiao-Ming], Zhang, T.Y.[Tian-Yu], Huang, B.Q.[Bing-Qing], Jia, T.[Tong],
Capabilities of Chinese Gaofen-3 Synthetic Aperture Radar in Selected Topics for Coastal and Ocean Observations,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Wei, S.[Sisi], Zhang, H.[Hong], Wang, C.[Chao], Wang, Y.Y.[Yuan-Yuan], Xu, L.[Lu],
Multi-Temporal SAR Data Large-Scale Crop Mapping Based on U-Net Model,
RS(11), No. 1, 2019, pp. xx-yy.
DOI Link 1901

Liu, S.J.[Sheng-Jie], Qi, Z.X.[Zhi-Xin], Li, X.[Xia], Yeh, A.G.O.[Anthony Gar-On],
Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903

Ustuner, M.[Mustafa], Sanli, F.B.[Fusun Balik],
Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation,
IJGI(8), No. 2, 2019, pp. xx-yy.
DOI Link 1903

Mohammadimanesh, F.[Fariba], Salehi, B.[Bahram], Mahdianpari, M.[Masoud], Gill, E.[Eric], Molinier, M.[Matthieu],
A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem,
PandRS(151), 2019, pp. 223-236.
Elsevier DOI 1904
Deep learning, Land cover, Wetland, Convolutional Neural Network (CNN), Polarimetric Synthetic Aperture Radar (PolSAR) BibRef

Xie, Q.H.[Qing-Hua], Wang, J.F.[Jin-Fei], Liao, C.H.[Chun-Hua], Shang, J.L.[Jia-Li], Lopez-Sanchez, J.M.[Juan M.], Fu, H.Q.[Hai-Qiang], Liu, X.[Xiuguo],
On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904

Teimouri, N.[Nima], Dyrmann, M.[Mads], Jørgensen, R.N.[Rasmus Nyholm],
A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905

Ni, K.[Kang], Wu, Y.Q.[Yi-Quan], Wang, P.[Peng],
Scene Classification from Synthetic Aperture Radar Images Using Generalized Compact Channel-Boosted High-Order Orderless Pooling Network,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905

Sonobe, R.[Rei],
Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906

Mahdianpari, M.[Masoud], Mohammadimanesh, F.[Fariba], McNairn, H.[Heather], Davidson, A.[Andrew], Rezaee, M.[Mohammad], Salehi, B.[Bahram], Homayouni, S.[Saeid],
Mid-season Crop Classification Using Dual-, Compact-, and Full-Polarization in Preparation for the Radarsat Constellation Mission (RCM),
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907

Valcarce-Diñeiro, R.[Rubén], Arias-Pérez, B.[Benjamín], Lopez-Sanchez, J.M.[Juan M.], Sánchez, N.[Nilda],
Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907

Yang, R., Xu, X., Xu, Z., Dong, H., Gui, R., Pu, F.,
Dynamic Fractal Texture Analysis for PolSAR Land Cover Classification,
GeoRS(57), No. 8, August 2019, pp. 5991-6002.
fractals, geophysical image processing, geophysical techniques, image classification, image sequences, image texture, land cover, polarimetric synthetic-aperture radar (PolSAR) BibRef

La Rosa, L.E.C.[Laura Elena Cué], Feitosa, R.Q.[Raul Queiroz], Happ, P.N.[Patrick Nigri], Sanches, I.D.[Ieda Del'Arco], da Costa, G.A.O.P.[Gilson Alexandre Ostwald Pedro],
Combining Deep Learning and Prior Knowledge for Crop Mapping in Tropical Regions from Multitemporal SAR Image Sequences,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909

Sunaga, Y., Natsuaki, R., Hirose, A.,
Land Form Classification and Similar Land-Shape Discovery by Using Complex-Valued Convolutional Neural Networks,
GeoRS(57), No. 10, October 2019, pp. 7907-7917.
convolutional neural nets, feature extraction, geophysical signal processing, image classification, interferometric synthetic aperture radar (InSAR) BibRef

Guo, J.[Jiao], Li, H.H.[Heng-Hui], Ning, J.F.[Ji-Feng], Han, W.T.[Wen-Ting], Zhang, W.T.[Wei-Tao], Zhou, Z.S.[Zheng-Shu],
Feature Dimension Reduction Using Stacked Sparse Auto-Encoders for Crop Classification with Multi-Temporal, Quad-Pol SAR Data,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001

Zhang, W.T.[Wei-Tao], Wang, M.[Min], Guo, J.[Jiao], Lou, S.T.[Shun-Tian],
Crop Classification Using MSCDN Classifier and Sparse Auto-Encoders with Non-Negativity Constraints for Multi-Temporal, Quad-Pol SAR Data,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107

Qadir, A.[Abdul], Mondal, P.[Pinki],
Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002

Oré, G.[Gian], Alcântara, M.S.[Marlon S.], Góes, J.A.[Juliana A.], Oliveira, L.P.[Luciano P.], Yepes, J.[Jhonnatan], Teruel, B.[Bárbara], Castro, V.[Valquíria], Bins, L.S.[Leonardo S.], Castro, F.[Felicio], Luebeck, D.[Dieter], Moreira, L.F.[Laila F.], Gabrielli, L.H.[Lucas H.], Hernandez-Figueroa, H.E.[Hugo E.],
Crop Growth Monitoring with Drone-Borne DInSAR,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003

Liao, C.H.[Chun-Hua], Wang, J.F.[Jin-Fei], Xie, Q.H.[Qing-Hua], Baz, A.A.[Ayman Al], Huang, X.D.[Xiao-Dong], Shang, J.L.[Jia-Li], He, Y.J.[Yong-Jun],
Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003

Dias Soares, M.[Marinalva], Dutra, L.V.[Luciano Vieira], Pedro da Costa, G.A.O.[Gilson Alexandre Ostwald], Queiroz Feitosa, R.[Raul], Galante Negri, R.[Rogério], Diaz, P.M.A.[Pedro M. A.],
A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003

Ren, B., Hou, B., Chanussot, J., Jiao, L.,
PolSAR Feature Extraction Via Tensor Embedding Framework for Land Cover Classification,
GeoRS(58), No. 4, April 2020, pp. 2337-2351.
Feature extraction, Matrix decomposition, Synthetic aperture radar, Task analysis, Scattering, tensor embedding framework BibRef

Busquier, M.[Mario], Lopez-Sanchez, J.M.[Juan M.], Mestre-Quereda, A.[Alejandro], Navarro, E.[Elena], González-Dugo, M.P.[María P.], Mateos, L.[Luciano],
Exploring TanDEM-X Interferometric Products for Crop-Type Mapping,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Liu, C.S.[Chun-Sheng], Wang, Z.M.[Zhong-Mei],
Efficient complex ISAR object recognition using adaptive deep relation learning,
IET-CV(14), No. 5, August 2020, pp. 185-191.
DOI Link 2007
inverse synthetic aperture radar BibRef

Monti-Guarnieri, A.[Andrea], Manzoni, M.[Marco], Giudici, D.[Davide], Recchia, A.[Andrea], Tebaldini, S.[Stefano],
Vegetated Target Decorrelation in SAR and Interferometry: Models, Simulation, and Performance Evaluation,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008

Hong, D.F.[Dan-Feng], Yokoya, N.[Naoto], Xia, G.S.[Gui-Song], Chanussot, J.[Jocelyn], Zhu, X.X.[Xiao Xiang],
X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data,
PandRS(167), 2020, pp. 12-23.
Elsevier DOI 2008
Adversarial, Cross-modality, Deep learning, Deep neural network, Fusion, Hyperspectral, Multispectral, Mutual learning, Synthetic aperture radar BibRef

Quan, Y.H.[Ying-Hui], Tong, Y.P.[Ying-Ping], Feng, W.[Wei], Dauphin, G.[Gabriel], Huang, W.J.[Wen-Jiang], Xing, M.D.[Meng-Dao],
A Novel Image Fusion Method of Multi-Spectral and SAR Images for Land Cover Classification,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

Ajadi, O.A.[Olaniyi A.], Liao, H.M.[He-Ming], Jaacks, J.[Jason], Santos, A.D.[Alfredo Delos], Kumpatla, S.P.[Siva P.], Patel, R.[Rinkal], Swatantran, A.[Anu],
Landscape-Scale Crop Lodging Assessment across Iowa and Illinois Using Synthetic Aperture Radar (SAR) Images,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012

Sun, Z.S.[Zhen-Sheng], Liu, M.[Miao], Liu, P.[Peng], Li, J.[Juan], Yu, T.[Tao], Gu, X.F.[Xing-Fa], Yang, J.[Jian], Mi, X.F.[Xiao-Fei], Cao, W.J.[Wei-Jia], Zhang, Z.W.[Zhou-Wei],
SAR Image Classification Using Fully Connected Conditional Random Fields Combined with Deep Learning and Superpixel Boundary Constraint,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

Sun, Z.S.[Zhen-Sheng], Li, J.[Juan], Liu, P.[Peng], Cao, W.J.[Wei-Jia], Yu, T.[Tao], Gu, X.F.[Xing-Fa],
SAR Image Classification Using Greedy Hierarchical Learning With Unsupervised Stacked CAEs,
GeoRS(59), No. 7, July 2021, pp. 5721-5739.
Synthetic aperture radar, Remote sensing, Feature extraction, Training, Convolution, Speckle, Machine learning, synthetic aperture radar (SAR) BibRef

Wu, Z., Hou, B., Jiao, L.,
Multiscale CNN With Autoencoder Regularization Joint Contextual Attention Network for SAR Image Classification,
GeoRS(59), No. 2, February 2021, pp. 1200-1213.
Radar polarimetry, Feature extraction, Synthetic aperture radar, Image reconstruction, Training, Decoding, Deep learning, synthetic aperture radar (SAR) BibRef

Liang, W.K.[Wen-Kai], Wu, Y.[Yan], Li, M.[Ming], Cao, Y.[Yice], Hu, X.[Xin],
High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

Gella, G.W.[Getachew Workineh], Bijker, W.[Wietske], Belgiu, M.[Mariana],
Mapping crop types in complex farming areas using SAR imagery with dynamic time warping,
PandRS(175), 2021, pp. 171-183.
Elsevier DOI 2105
Crop type mapping, Decision level fusion, Sentinel-1, TerraSAR-X, Time Weighted Dynamic Time Warping BibRef

Zhang, B.[Bin], Chang, L.[Ling], Stein, A.[Alfred],
Spatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images,
PandRS(176), 2021, pp. 222-236.
Elsevier DOI 2106
And: Corrigendum: PandRS(180), 2021, pp. 335.
Elsevier DOI 2109
Spatio-temporal data integration, Geolocation uncertainty, Monte Carlo methods, Multiple Hypothesis Testing, InSAR time series analysis BibRef

Cheng, J.[Jianda], Zhang, F.[Fan], Xiang, D.L.[De-Liang], Yin, Q.[Qiang], Zhou, Y.S.[Yong-Sheng], Wang, W.[Wei],
PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

Serafino, F.[Francesco], Bianco, A.[Andrea],
Use of X-Band Radars to Monitor Small Garbage Islands,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109

Liu, Y.Q.[Yi-Qing], Zhao, W.Z.[Wen-Zhi], Chen, S.[Shuo], Ye, T.[Tao],
Mapping Crop Rotation by Using Deeply Synergistic Optical and SAR Time Series,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110

Arii, M.[Motofumi], Yamada, H.[Hiroyoshi], Sakamoto, H.[Hitoshi], Kojima, S.[Shoichiro],
Sensitivity Study of X-Band Multiparametric SAR Data From Cabbage Fields,
GeoRS(60), 2022, pp. 1-11.
Scattering, Synthetic aperture radar, Backscatter, Urban areas, Radar polarimetry, Radar, Vegetation mapping, Cabbage field, multiparametric synthetic aperture radar (SAR) BibRef

Ghosh, S.S.[Swarnendu Sekhar], Dey, S.[Subhadip], Bhogapurapu, N.[Narayanarao], Homayouni, S.[Saeid], Bhattacharya, A.[Avik], McNairn, H.[Heather],
Gaussian Process Regression Model for Crop Biophysical Parameter Retrieval from Multi-Polarized C-Band SAR Data,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202

Jin, H.[Huiran], Mountrakis, G.[Giorgos],
Fusion of optical, radar and waveform LiDAR observations for land cover classification,
PandRS(187), 2022, pp. 171-190.
Elsevier DOI 2205
Fusion, Land cover classification, Optical, SAR, Waveform LiDAR, Accuracy BibRef

Xie, Q.H.[Qing-Hua], Dou, Q.[Qi], Peng, X.[Xing], Wang, J.F.[Jin-Fei], Lopez-Sanchez, J.M.[Juan M.], Shang, J.L.[Jia-Li], Fu, H.Q.[Hai-Qiang], Zhu, J.J.[Jian-Jun],
Crop Classification Based on the Physically Constrained General Model-Based Decomposition Using Multi-Temporal RADARSAT-2 Data,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206

Li, H.P.[He-Ping], Lu, J.[Jing], Tian, G.X.[Gui-Xiang], Yang, H.J.[Hui-Jin], Zhao, J.H.[Jian-Hui], Li, N.[Ning],
Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Yang, M.J.[Mei-Juan], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Hou, B.[Biao], Yang, S.Y.[Shu-Yuan], Zhang, Y.[Yake], Wang, J.L.[Jian-Long],
Coarse-to-Fine Contrastive Self-Supervised Feature Learning for Land-Cover Classification in SAR Images With Limited Labeled Data,
IP(31), 2022, pp. 6502-6516.
Task analysis, Feature extraction, Radar polarimetry, Semantics, Decoding, Synthetic aperture radar, Self-supervised learning, land-cover classification BibRef

Qin, X.L.[Xing-Li], Zhao, L.L.[Ling-Li], Yang, J.[Jie], Li, P.X.[Ping-Xiang], Wu, B.F.[Bing-Fang], Sun, K.[Kaimin], Xu, Y.B.[Yu-Bin],
Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212

Pepe, A.[Antonio],
A 3D Space-Time Non-Local Mean Filter (NLMF) for Land Changes Retrieval with Synthetic Aperture Radar Images,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212

Yuan, Y.[Yuan], Lin, L.[Lei], Zhou, Z.G.[Zeng-Guang], Jiang, H.J.[Hou-Jun], Liu, Q.S.[Qing-Shan],
Bridging optical and SAR satellite image time series via contrastive feature extraction for crop classification,
PandRS(195), 2023, pp. 222-232.
Elsevier DOI 2301
Contrastive learning, Crop classification, Feature extraction, Satellite image time series (SITS), Synthetic aperture radar (SAR) BibRef

Wang, H.X.[Hong-Xia], Yang, H.R.[Hao-Ran], Huang, Y.[Yabo], Wu, L.[Lin], Guo, Z.W.[Zheng-Wei], Li, N.[Ning],
Classification of Land Cover in Complex Terrain Using Gaofen-3 SAR Ascending and Descending Orbit Data,
RS(15), No. 8, 2023, pp. 2177.
DOI Link 2305

Huang, Y.[Yabo], Meng, M.M.[Meng-Meng], Hou, Z.[Zhuoyan], Wu, L.[Lin], Guo, Z.W.[Zheng-Wei], Shen, X.[Xiajiong], Zheng, W.[Wenkui], Li, N.[Ning],
Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index,
RS(15), No. 13, 2023, pp. 3221.
DOI Link 2307

Saadati, M.[Mirmohammad], Pedersoli, M.[Marco], Cardinal, P.[Patrick], Oliver, P.[Peter],
Radarsat-2 Synthetic-aperture Radar Land Cover Segmentation Using Deep Convolutional Neural Networks,
Springer DOI 2103

Schmitz, S., Weinmann, M., Thiele, A.,
Incorporating Interferometric Coherence Into LULC Classification Of Airborne Polsar-images Using Fully Convolutional Networks,
DOI Link 2012

Wang, W., Tian, Z., Tian, B., Zhang, J.,
Supervised Manifold-learning Algorithm for Polsar Feature Extraction and LULC Classification,
DOI Link 2012

Kiana, E., Homayouni, S., Sharifi, M.A., Farid-Rohani, M.R.,
Comparison of Decomposition Methods Over Agricultural Fields Using The Uavsar Polarimetric Synthetic Aperture Radar,
DOI Link 1912

Park, S., Im, J.,
Classification Of Croplands Through Fusion Of Optical And Sar Time Series Data,
ISPRS16(B7: 703-704).
DOI Link 1610

Bougarradh, A.[Ahlem], Mhiri, S.[Slim], Ghorbel, F.[Faouzi],
Unsupervised Classification of Synthetic Aperture Radar Imagery Using a Bootstrap Version of the Generalized Mixture Expectation Maximization Algorithm,
WWW Link. 1606

Dehghan-Soraki, Y., Saha, S.K., Kumari, M.,
A Modified Polarimetric Decompostion for Applicabilty in Complex Agricultural Environment,
DOI Link 1311

Mahdian, M., Homayouni, S., Fazel, M.A., Mohammadimanesh, F.,
Agricultural Land Classification Based on Statistical Analysis of Full Polarimetric SAR Data,
DOI Link 1311

Khabazan, S., Motagh, M., Hosseini, M.,
Evaluation of Radar Backscattering Models IEM, OH, and Dubois using L and C-Bands SAR Data over different vegetation canopy covers and soil depths,
DOI Link 1311

Xu, J., Li, Z., Tian, B., Chen, Q., Zhang, P.,
Classification of Polarimetric SAR Image Based on the Subspace Method,
DOI Link 1311

Li, P.X., Sun, W.D., Yang, J., Shi, L., Lang, F.K., Jiang, W.,
High Resolution POLSAR Image Classification Based on Genetic Algorithm and Support Vector Machine,
DOI Link 1311

Fu, H.Q.[Hai-Qiang], Zhu, J.J.[Jian-Jun], Wang, C.C.[Chang-Cheng], Xie, Q.H.[Qing-Hua], Zhao, R.[Rong],
A Robust PCT Method Based on Complex Least Squares Adjustment Method,
DOI Link 1311
PCT: Polarization Coherence Tomography. Vegetation heights. BibRef

Singh, D., Chamundeeswari, V.V.[V. Vijaya],
Labeling of Clusters Based on Critical Analysis of Texture Measures,
DOI Link 1308

Mishra, B.[Bhogendra], Susaki, J.[Junichi],
Generation of pseudo-fully polarimetric data from dual polarimetric data for land cover classification,

Qin, X.X.[Xian-Xiang], Zhou, S.L.[Shi-Lin], Zou, H.X.[Huan-Xin], Gao, G.[Gui],
Statistical modeling of sea clutter in high-resolution SAR images using generalized gamma distribution,

Chu, H.T., Ge, L.,
Combination Of Genetic Algorithm And Dempster-shafer Theory Of Evidence For Land Cover Classification Using Integration of SAR and Optical Satellite Imagery,
DOI Link 1209

Recio, J.A., Ruiz, L.Á.[Luis Á.], Hermosilla, T.[Txomin], Herrera-Cruz, V., Fernández-Sarría, A.,
Combination of TERRASAR-X and Optical Imagery for LU/LC Mapping using an Object-Based Approach,
PDF File. 1106
Also use backscattering informaton. BibRef

Liu, Z.Y.[Zhen-Yu], Yu, J.[Jie], Jan, Q.[Qin], Zhao, Z.[Zheng], Yang, J.[Jie],
Investigation of Vegetation Phases Extraction Based on Polarmetric SAR Interferometry,

Power, D.[Desmond], Adlakha, P.[Paul], Dragosevic, M.[Marina], McGuire, P.[Peter], Vachon, P.[Paris], Meunier, P.[Pierre],
Critical infrastructure monitoring using high resolution SAR satellites,
PDF File. 1006

Lohmann, P.[Peter], Soergel, U., Tavakkoli, M., Farghaly, D.,
Multi-temporal Classification for Crop Discrimination using TerraSAR-X Spotlight images,
PDF File. 0906

Riedel, T., Thiel, C., Schmullius, C.,
An object-based classification procedure for the derivation of broad land cover classes using both optical and SAR data,
PDF File. 0607

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Sentinel-1, -2, -3 for Land Cover, Remote Sensing .

Last update:Aug 31, 2023 at 09:37:21