{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:38:50Z","timestamp":1760143130512,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:00:00Z","timestamp":1705449600000},"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":["2023YFB3904900"],"award-info":[{"award-number":["2023YFB3904900"]}],"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>Machine learning and deep neural networks have shown satisfactory performance in the supervised classification of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, the PolSAR image classification task still faces some challenges. First, the current form of model input used for this task inevitably involves tedious preprocessing. In addition, issues such as insufficient labels and the design of the model also affect classification performance. To address these issues, this study proposes an augmentation method to better utilize the labeled data and improve the input format of the model, and an end-to-end PolSAR image global classification is implemented on our proposed hybrid network, PolSARMixer. Experimental results demonstrate that, compared to existing methods, our proposed method reduces the steps for the classification of PolSAR images, thus eliminating repetitive data preprocessing procedures and significantly improving classification performance.<\/jats:p>","DOI":"10.3390\/rs16020380","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T03:50:56Z","timestamp":1705549856000},"page":"380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Global Polarimetric Synthetic Aperture Radar Image Segmentation with Data Augmentation and Hybrid Architecture Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6813-7608","authenticated-orcid":false,"given":"Zehua","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215128, China"},{"name":"Suzhou Aerospace Information Research Institute, Suzhou 215128, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China"},{"name":"School of Electronic, Electrical and Communication, Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zezhong","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215128, China"},{"name":"Suzhou Aerospace Information Research Institute, Suzhou 215128, China"}]},{"given":"Xiaolan","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215128, China"},{"name":"Suzhou Aerospace Information Research Institute, Suzhou 215128, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China"},{"name":"School of Electronic, Electrical and Communication, Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3192-3476","authenticated-orcid":false,"given":"Zhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215128, China"},{"name":"Suzhou Aerospace Information Research Institute, Suzhou 215128, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China"},{"name":"School of Electronic, Electrical and Communication, Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"National Key Laboratory of Microwave Imaging Technology, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/19479832.2019.1655489","article-title":"Classification of SAR and PolSAR images using deep learning: A review","volume":"11","author":"Parikh","year":"2020","journal-title":"Int. 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