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

Haralick, R.M.[Robert M.], Caspall, F., and Simonett, D.S.,
Using Radar Imagery for Crop Discrimination: A Statistical and Conditional Probability Study,
RSE(1), 1970, pp. 131-142. BibRef 7000

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
BibRef

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.
IEEE DOI 2008
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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1106

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.
IEEE DOI 1106
BibRef

Burgin, M., Clewley, D., Lucas, R.M., Moghaddam, M.,
A Generalized Radar Backscattering Model Based on Wave Theory for Multilayer Multispecies Vegetation,
GeoRS(49), No. 12, December 2011, pp. 4832-4845.
IEEE DOI 1201
BibRef

Clewley, D.[Daniel], Bunting, P.[Peter], Shepherd, J.[James], Gillingham, S.[Sam], Flood, N.[Neil], Dymond, J.[John], Lucas, R.[Richard], Armston, J.[John], Moghaddam, M.[Mahta],
A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables,
RS(6), No. 7, 2014, pp. 6111-6135.
DOI Link 1408
BibRef

Laurila, H., Karjalainen, M., Kleemola, J., Hyyppä, J.,
Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing,
RS(2), No. 9, September 2010, pp. 2185-2239.
DOI Link 1203
BibRef

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.
IEEE DOI 1204
BibRef

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.
IEEE DOI 1210
BibRef

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.
IEEE DOI 1205
BibRef

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.
IEEE DOI 1303
BibRef

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.
IEEE DOI 1403
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.
IEEE DOI 1403
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
BibRef

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
Crops
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1403
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.
IEEE DOI 2009
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,
MultiTemp15(1-4)
IEEE DOI 1511
radar imaging BibRef

Alonso-Gonzalez, A., Lopez-Martinez, C., Hajnsek, I.,
Processing polarimetric SAR time series over urban areas with binary partition trees,
MultiTemp15(1-4)
IEEE DOI 1511
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.
IEEE DOI 1303
BibRef

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.
IEEE DOI 1303
BibRef
Earlier:
Hyperspectral image segmentation using Binary Partition Trees,
ICIP11(1273-1276).
IEEE DOI 1201
BibRef
Earlier:
Comparison of merging orders and pruning strategies for Binary Partition Tree in hyperspectral data,
ICIP10(2565-2568).
IEEE DOI 1009

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.
IEEE DOI 1502
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.
IEEE DOI 1601
Accuracy BibRef

Dargahi, A., Maghsoudi, Y., Abkar, A.A.,
Supervised Classification of Polarimetric SAR Imagery Using Temporal and Contextual Information,
SMPR13(107-110).
DOI Link 1311
BibRef

Larrañaga, A.[Arantzazu], Álvarez-Mozos, J.[Jesús],
On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery,
RS(8), No. 4, 2016, pp. 335.
DOI Link 1604
BibRef

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
BibRef

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.
IEEE DOI 1610
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1708
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.
IEEE DOI 1711
Dielectrics, 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
BibRef

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.
IEEE DOI 1804
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.
IEEE DOI 1804
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.
IEEE DOI 1904
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.
IEEE DOI 1809
Synthetic aperture radar, Feature extraction, Polarimetry, Satellites, Polarization, Data mining, Image classification, synthetic aperture radar (SAR) BibRef

Vaduva, C.[Corina], Danisor, 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.
IEEE DOI 1810
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
BibRef
And: Erratum: RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

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.
IEEE DOI 1812
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1908
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
BibRef

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.
IEEE DOI 1910
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
BibRef

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
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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.
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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
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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,
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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,
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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.
IEEE DOI 2004
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.
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Liu, C.S.[Chun-Sheng], Wang, Z.M.[Zhong-Mei],
Efficient complex ISAR object recognition using adaptive deep relation learning,
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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
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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
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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
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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,
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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.
IEEE DOI 2106
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.
IEEE DOI 2101
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.
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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
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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
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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
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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
BibRef

Ofori-Ampofo, S.[Stella], Pelletier, C.[Charlotte], Lang, S.[Stefan],
Crop Type Mapping from Optical and Radar Time Series Using Attention-Based Deep Learning,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Agbona, A.[Afolabi], Teare, B.[Brody], Ruiz-Guzman, H.[Henry], Dobreva, I.D.[Iliyana D.], Everett, M.E.[Mark E.], Adams, T.[Tyler], Montesinos-Lopez, O.A.[Osval A.], Kulakow, P.A.[Peter A.], Hays, D.B.[Dirk B.],
Prediction of Root Biomass in Cassava Based on Ground Penetrating Radar Phenomics,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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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.
IEEE DOI 2112
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
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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
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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
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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.
IEEE DOI 2211
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
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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
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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
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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
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Pasternak, M.[Marta], Pawluszek-Filipiak, K.[Kamila],
Evaluation of C and X-Band Synthetic Aperture Radar Derivatives for Tracking Crop Phenological Development,
RS(15), No. 20, 2023, pp. 4996.
DOI Link 2310
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Zhang, W.T.[Wei-Tao], Liu, L.[Lu], Bai, Y.[Yv], Li, Y.B.[Yi-Bang], Guo, J.[Jiao],
Crop classification based on multi-temporal PolSAR images with a single tensor network,
PR(143), 2023, pp. 109773.
Elsevier DOI 2310
Polarimetric synthetic aperture radar (PolSAR), Crop classification, Tensor affine transformation network (TATN) BibRef

Han, Z.[Zhu], Zhang, C.[Ce], Gao, L.[Lianru], Zeng, Z.Q.[Zhi-Qiang], Zhang, B.[Bing], Atkinson, P.M.[Peter M.],
Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery,
PandRS(206), 2023, pp. 293-310.
Elsevier DOI Code:
WWW Link. 2312
Crop mapping, Time-series imagery, Spatio-temporal self-attention, Multi-scale cross-attention, Deep learning BibRef

Mirzaei, A.[Ali], Bagheri, H.[Hossein], Khosravi, I.[Iman],
Enhancing Crop Classification Accuracy through Synthetic SAR-Optical Data Generation Using Deep Learning,
IJGI(12), No. 11, 2023, pp. xx-yy.
DOI Link 2312
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Quan, Y.J.[Yu-Jun], Zhang, R.R.[Rong-Rong], Li, J.[Jian], Ji, S.[Song], Guo, H.[Hengliang], Yu, A.[Anzhu],
Learning SAR-Optical Cross Modal Features for Land Cover Classification,
RS(16), No. 2, 2024, pp. 431.
DOI Link 2402
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Kwak, G.H.[Geun-Ho], Park, N.W.[No-Wook],
Assessing the Potential of Multi-Temporal Conditional Generative Adversarial Networks in SAR-to-Optical Image Translation for Early-Stage Crop Monitoring,
RS(16), No. 7, 2024, pp. 1199.
DOI Link 2404
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Wang, Y.Y.[Yang-Yang], Zhang, W.G.[Wen-Gang], Chen, W.D.[Wei-Dong], Chen, C.[Chang],
BSDSNet: Dual-Stream Feature Extraction Network Based on Segment Anything Model for Synthetic Aperture Radar Land Cover Classification,
RS(16), No. 7, 2024, pp. 1150.
DOI Link 2404
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Wang, Y.Y.[Yang-Yang], Zhang, W.G.[Wen-Gang], Chen, W.D.[Wei-Dong], Chen, C.[Chang], Liang, Z.Y.[Zhen-Yu],
MFFnet: Multimodal Feature Fusion Network for Synthetic Aperture Radar and Optical Image Land Cover Classification,
RS(16), No. 13, 2024, pp. 2459.
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Moharrami, M.[Meysam], Attarchi, S.[Sara], Gloaguen, R.[Richard], Alavipanah, S.K.[Seyed Kazem],
Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping,
RS(16), No. 9, 2024, pp. 1566.
DOI Link 2405
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Sorokin, A.[Aleksei], Stepanov, A.[Alexey], Dubrovin, K.[Konstantin], Verkhoturov, A.[Andrey],
Enhancement of Comparative Assessment Approaches for Synthetic Aperture Radar (SAR) Vegetation Indices for Crop Monitoring and Identification: Khabarovsk Territory (Russia) Case Study,
RS(16), No. 14, 2024, pp. 2532.
DOI Link 2408
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Wittstruck, L.[Lucas], Jarmer, T.[Thomas], Waske, B.[Björn],
Multi-Stage Feature Fusion of Multispectral and SAR Satellite Images for Seasonal Crop-Type Mapping at Regional Scale Using an Adapted 3D U-Net Model,
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Schmitz, S., Weinmann, M., Thiele, A.,
Incorporating Interferometric Coherence Into LULC Classification Of Airborne Polsar-images Using Fully Convolutional Networks,
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Wang, W., Tian, Z., Tian, B., Zhang, J.,
Supervised Manifold-learning Algorithm for Polsar Feature Extraction and LULC Classification,
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DOI Link 2012
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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,
SMPR19(649-653).
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Park, S., Im, J.,
Classification Of Croplands Through Fusion Of Optical And Sar Time Series Data,
ISPRS16(B7: 703-704).
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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,
ICISP16(273-282).
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Dehghan-Soraki, Y., Saha, S.K., Kumari, M.,
A Modified Polarimetric Decompostion for Applicabilty in Complex Agricultural Environment,
SMPR13(133-137).
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Mahdian, M., Homayouni, S., Fazel, M.A., Mohammadimanesh, F.,
Agricultural Land Classification Based on Statistical Analysis of Full Polarimetric SAR Data,
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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,
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Xu, J., Li, Z., Tian, B., Chen, Q., Zhang, P.,
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Li, P.X., Sun, W.D., Yang, J., Shi, L., Lang, F.K., Jiang, W.,
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Fu, H.Q.[Hai-Qiang], Zhu, J.J.[Jian-Jun], Wang, C.C.[Chang-Cheng], Xie, Q.H.[Qing-Hua], Zhao, R.[Rong],
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Singh, D., Chamundeeswari, V.V.[V. Vijaya],
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Qin, X.X.[Xian-Xiang], Zhou, S.L.[Shi-Lin], Zou, H.X.[Huan-Xin], Gao, G.[Gui],
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Chu, H.T., Ge, L.,
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Recio, J.A., Ruiz, L.Á.[Luis Á.], Hermosilla, T.[Txomin], Herrera-Cruz, V., Fernández-Sarría, A.,
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Sentinel-1, -2, -3 for Land Cover, Remote Sensing .