{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T09:20:55Z","timestamp":1775640055595,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021YFC2803304"],"award-info":[{"award-number":["2021YFC2803304"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFB3902404"],"award-info":[{"award-number":["2022YFB3902404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations were designed, and the classification accuracy of various approaches was evaluated using the classic convolutional neural networks (CNNs) AlexNet and VGG16. The findings reveal that the commonly employed six-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete nine-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other seven schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition.<\/jats:p>","DOI":"10.3390\/rs16111826","type":"journal-article","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T08:54:28Z","timestamp":1716281668000},"page":"1826","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Research on Input Schemes for Polarimetric SAR Classification Using Deep Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Shuaiying","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Electronic Science and Engineering, National University of Defense Technology (NUDT), Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4977-7577","authenticated-orcid":false,"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Xia","sequence":"additional","affiliation":[{"name":"China Centre Resources Satellite Data and Application, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Engineering, National University of Defense Technology (NUDT), Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"An","sequence":"additional","affiliation":[{"name":"National Satellite Ocean Application Service, Beijing 100081, China"},{"name":"Key Laboratory of Space Ocean Remote Sensing and Applications, Ministry of Natural Resources, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/TGRS.2008.916326","article-title":"POLSAR Image Analysis of Wetlands Using a Modified Four-Component Scattering Power Decomposition","volume":"46","author":"Yajima","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4410118","DOI":"10.1109\/TGRS.2023.3327109","article-title":"CNN-improved Superpixel-to-pixel Fuzzy Graph Convolution Network for PolSAR Image Classification","volume":"61","author":"Shi","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4008805","DOI":"10.1109\/LGRS.2023.3298898","article-title":"PolSAR Ship Detection Based on Noncircularity and Oblique Subspace Projection","volume":"20","author":"Gu","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ji, Y., Dong, Z., Zhang, Y., Tang, F., Mao, W., Zhao, H., Xu, Z., Zhang, Q., Zhao, B., and Gao, H. (Engineering, 2024). Equatorial Ionospheric Scintillation Measurement in Advanced Land Observing Satellite (ALOS) Phased Array-Type L-Band Synthetic Aperture Radar (PALSAR) Observations, Engineering, in press.","DOI":"10.1016\/j.eng.2024.01.027"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1109\/JSTARS.2023.3330752","article-title":"Drifting ionospheric scintillation simulation for L-band geosynchronous SAR","volume":"17","author":"Tang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"\u2018An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1117\/12.140636","article-title":"Physical reality of radar targets","volume":"1748","author":"Huynen","year":"1993","journal-title":"Proc. SPIE"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1080\/01431169408954244","article-title":"Classification of multi-look polarimetric SAR data based on complex Wishart distribution","volume":"15","author":"Lee","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, F., Li, P., Zhang, Y., Liu, X., Ma, X., and Yin, Z. (2023, January 7\u20139). A Enhanced DeepLabv3+ for PolSAR image classification. Proceedings of the 2023 4th International Conference on Computer Engineering and Application (ICCEA), Hangzhou, China.","DOI":"10.1109\/ICCEA58433.2023.10135214"},{"key":"ref_11","first-page":"5207419","article-title":"Learning Scattering Similarity and Texture-Based Attention with Convolutional Neural Networks for PolSAR Image Classification","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4403615","DOI":"10.1109\/TGRS.2021.3093474","article-title":"A Deep Reinforcement Learning-Based Framework for PolSAR Imagery Classification","volume":"60","author":"Nie","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","unstructured":"Ulaby, F.T., and Elachi, C. (1990). Geocarto International, Artech House. Available online: http:\/\/www.informaworld.com."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1016\/j.patcog.2012.06.022","article-title":"Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary","volume":"46","author":"Yang","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, X., Han, T.X., and Yan, S. (October, January 29). An HOG-LBP human detector with partial occlusion handing. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan.","DOI":"10.1109\/ICCV.2009.5459207"},{"key":"ref_16","first-page":"2169","article-title":"Beyond bags of features: Spatial pyranic matching recognizing natural scene categories","volume":"13","author":"Lazebnik","year":"2006","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, Q., Li, L., Xu, Q., Yang, S., Shi, X., and Liu, X. (2011). Multi-feature segmentation for high-resolution polarimetric SAR data based on fractal net evolution approach. Remote Sens., 9.","DOI":"10.3390\/rs9060570"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hua, W., Wang, S., Xie, W., Guo, Y., and Jin, X. (August, January 28). Dual-channel convolutional neural network for polarimetric SAR images classification. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899103"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1109\/JSTARS.2018.2851023","article-title":"Patch-sorted deep feature learning for high resolution SAR image classification","volume":"11","author":"Ren","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kilbride, J.B., Poortinga, A., Bhandari, B., Thwal, N.S., Quyen, N.H., Silverman, J., Tenneson, K., Bell, D., Gregory, M., and Kennedy, R. (2023). A Near Real-Time Mapping of Tropical Forest Disturbance Using SAR and Semantic Segmentation in Google Earth Engine. Remote Sens., 15.","DOI":"10.3390\/rs15215223"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109965","DOI":"10.1016\/j.asoc.2022.109965","article-title":"Complex matrix and multi-feature collaborative learning for polarimetric SAR image classification","volume":"134","author":"Shi","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108922","DOI":"10.1016\/j.asoc.2022.108922","article-title":"Spatial feature-based convolutional neural network for PolSAR image classification","volume":"123","author":"Shang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1109\/TGRS.2012.2212445","article-title":"Classification of Sea Ice Types in ENVISAT Synthetic Aperture Radar Images","volume":"51","author":"Zakhvatkina","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, D., Wang, W., Gade, M., and Zhou, H. (2024). TENet: A Texture-Enhanced Network for Intertidal Sediment and Habitat Classification in Multiband PolSAR Images. Remote Sens., 16.","DOI":"10.3390\/rs16060972"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, L., Ji, D., Zhu, S., Gan, W., Wu, W., and Yan, J. (2021, January 20\u201325). Learning Statistical Texture for Semantic Segmentation. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01235"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, T., Yang, X., Wang, Y., Fang, J., and Jia, L. (2012, January 16\u201318). A multi-level SAR sea ice image classification method by incorporating egg-code-based expert knowledge. Proceedings of the 2012 5th International Congress on Image and Signal Processing (CISP), Chongqing, China.","DOI":"10.1109\/CISP.2012.6469789"},{"key":"ref_27","first-page":"80","article-title":"Research on Sea Ice Secondary Classification Method Using High-Resolution Fully Polarimetric Synthetic Aperture Radar Data","volume":"4","author":"Liu","year":"2013","journal-title":"Acta Oceanol. Sin."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, W., Gade, M., Stelzer, K., Kohlus, J., Zhao, X., and Fu, K. (2021). A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR. Remote Sens., 13.","DOI":"10.3390\/rs13030360"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"749950","DOI":"10.3389\/fenvs.2022.749950","article-title":"Random Forest Classification Method for Predicting Intertidal Wetland Migration Under Sea Level Rise","volume":"10","author":"Hughes","year":"2022","journal-title":"Front. Environ. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113554","DOI":"10.1016\/j.rse.2023.113554","article-title":"Multi- and hyperspectral classification of soft-bottom intertidal vegetation using a spectral library for coastal biodiversity remote sensing","volume":"290","author":"Davies","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/TGRS.2004.842108","article-title":"Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering","volume":"43","author":"Kersten","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, P., Zhang, X., Shi, L., Liu, M., Liu, G., Cao, C., and Wang, R. (2024). Assessment of Sea-Ice Classification Capabilities during Melting Period Using Airborne Multi-Frequency PolSAR Data. Remote Sens., 16.","DOI":"10.3390\/rs16061100"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3040","DOI":"10.1109\/TGRS.2018.2879984","article-title":"Polarimetric Convolutional Network for PolSAR Image Classification","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"17188","DOI":"10.1038\/s41598-020-74215-5","article-title":"Understanding deep learning in land use classification based on Sentinel-2 time series","volume":"10","author":"Atzberger","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"15365","DOI":"10.1038\/s41598-021-94422-y","article-title":"Semantic segmentation of PolSAR image data using advanced deep learning model","volume":"11","author":"Garg","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106390","DOI":"10.1016\/j.margeo.2020.106390","article-title":"Deep learning model for seabed sediment classification based on fuzzy ranking feature optimization","volume":"432","author":"Cui","year":"2021","journal-title":"Mar. Geol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1109\/LGRS.2018.2886559","article-title":"PolSAR Image Semantic Segmentation Based on Deep Transfer Learning\u2014Realizing Smooth Classification with Small Training Sets","volume":"16","author":"Wu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5218714","DOI":"10.1109\/TGRS.2021.3131986","article-title":"A Fine PolSAR Terrain Classification Algorithm Using the Texture Feature Fusion-Based Improved Convolutional Autoencoder","volume":"60","author":"Ai","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1109\/LGRS.2016.2618840","article-title":"Polarimetric SAR image classification using deep convolutional neural networks","volume":"13","author":"Zhou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/LGRS.2018.2799877","article-title":"PolSAR image classification using polarimetric-feature-driven deep convolutional neural network","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4493","DOI":"10.1109\/JSTARS.2020.3014966","article-title":"A new parallel dual-channel fully convolutional network via semi-supervised fcm for polsar image classification","volume":"13","author":"Feng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"30491","DOI":"10.1109\/ACCESS.2020.2973246","article-title":"A Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification","volume":"8","author":"Shi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3452","DOI":"10.1109\/TGRS.2010.2076285","article-title":"Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues","volume":"49","author":"Arii","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jafari, Z., Karami, E., Taylor, R., and Bobby, P. (2023). Enhanced Ship\/Iceberg Classification in SAR Images Using Feature Extraction and the Fusion of Machine Learning Algorithms. Remote Sens., 15.","DOI":"10.3390\/rs15215202"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ren, S., Zhou, F., and Bruzzone, L. (2024). Transfer-Aware Graph U-Net with Cross-Level Interactions for PolSAR Image Semantic Segmentation. Remote Sens., 16.","DOI":"10.3390\/rs16081428"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3919","DOI":"10.1109\/JSTARS.2019.2940973","article-title":"Optimal combination of polarimetric features for vegetation classification in PolSAR image","volume":"12","author":"Yin","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_48","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Proc. Neural Inf. Process. Syst."},{"key":"ref_49","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (1996, January 18\u201320). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA."},{"key":"ref_50","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/LGRS.2016.2597965","article-title":"Coastal Zone Classification with Fully Polarimetric SAR Imagery","volume":"13","author":"Gou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","first-page":"4007805","article-title":"A Multichannel Fusion Convolutional Neural Network Based on Scattering Mechanism for PolSAR Image Classification","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4006005","DOI":"10.1109\/LGRS.2020.3038240","article-title":"Terrain Segmentation in Polarimetric SAR Images Using Dual-Attention Fusion Network","volume":"19","author":"Xiao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4013305","DOI":"10.1109\/LGRS.2021.3073738","article-title":"A General Feature Paradigm for Unsupervised Cross-Domain PolSAR Image Classification","volume":"19","author":"Gui","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2116","DOI":"10.1109\/TGRS.2018.2871504","article-title":"A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification","volume":"57","author":"Bi","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5207118","DOI":"10.1109\/TGRS.2021.3071559","article-title":"Polarimetric Multipath Convolutional Neural Network for PolSAR Image Classification","volume":"60","author":"Cui","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1109\/TGRS.2005.852084","article-title":"Four-component scattering model for polarimetric SAR image decomposition","volume":"43","author":"Yamaguchi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","unstructured":"An, W. (2010). Research on Target Polarization Decomposition and Scattering Characteristic Extraction Based on Polarized SAR. [Ph.D. Dissertation, Tsinghua University]."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3649","DOI":"10.1109\/TGRS.2018.2886386","article-title":"A Reflection Symmetry Approximation of Multi-look Polarimetric SAR Data and its Application to Freeman-Durden Decomposition","volume":"57","author":"An","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"8019805","DOI":"10.1109\/LGRS.2021.3105684","article-title":"Modified Reflection Symmetry Decomposition and a New Polarimetric Product of GF-3","volume":"19","author":"An","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/TGRS.2010.2087763","article-title":"Nonlocal filtering for polarimetric SAR data: A pretest approach","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1980.4766994","article-title":"Digital image enhancement and noise filtering by use of local statistics","volume":"2","author":"Lee","year":"1980","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/7.53442","article-title":"Optimal speckle reduction in polarimetric SAR imagery","volume":"26","author":"Novak","year":"1990","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_65","first-page":"5512615","article-title":"Hyperspectral and multispectral classification for coastal wetland using depthwise feature interaction network","volume":"60","author":"Gao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","unstructured":"(2016). User Manual of Gaofen-3 Satellite Products, China Resources Satellite Application Center."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1109\/JOE.2017.2767106","article-title":"Ship classification in TerraSAR-X images with convolutional neural networks","volume":"43","author":"Bentes","year":"2018","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"7907","DOI":"10.1109\/TGRS.2019.2917214","article-title":"Land form classification and similar land-shape discovery by using complex-valued convolutional neural networks","volume":"57","author":"Sunaga","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"140303","DOI":"10.1007\/s11432-019-2772-5","article-title":"FUSAR-Ship: Building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition","volume":"63","author":"Hou","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_70","unstructured":"(2023, October 30). China Ocean Satellite Data Service System. Available online: https:\/\/osdds.nsoas.org.cn\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1826\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:45:49Z","timestamp":1760107549000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1826"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,21]]},"references-count":70,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16111826"],"URL":"https:\/\/doi.org\/10.3390\/rs16111826","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,21]]}}}