{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/polarimetric-convolutional-network-for-polsar","title":"Polarimetric Convolutional Network for PolSAR Image Classification","arxiv_id":"1807.02975","date":"2018-07-09","proceeding":null,"authors":["Xu Liu","Licheng Jiao","Xu Tang","Qigong Sun","Dan Zhang"],"abstract":"The approaches for analyzing the polarimetric scattering matrix of\npolarimetric synthetic aperture radar (PolSAR) data have always been the focus\nof PolSAR image classification. Generally, the polarization coherent matrix and\nthe covariance matrix obtained by the polarimetric scattering matrix only show\na limited number of polarimetric information. In order to solve this problem,\nwe propose a sparse scattering coding way to deal with polarimetric scattering\nmatrix and obtain a close complete feature. This encoding mode can also\nmaintain polarimetric information of scattering matrix completely. At the same\ntime, in view of this encoding way, we design a corresponding classification\nalgorithm based on convolution network to combine this feature. Based on sparse\nscattering coding and convolution neural network, the polarimetric\nconvolutional network is proposed to classify PolSAR images by making full use\nof polarimetric information. We perform the experiments on the PolSAR images\nacquired by AIRSAR and RADARSAT-2 to verify the proposed method. The\nexperimental results demonstrate that the proposed method get better results\nand has huge potential for PolSAR data classification. Source code for sparse\nscattering coding is available at\nhttps://github.com/liuxuvip/Polarimetric-Scattering-Coding.","url_abs":"http://arxiv.org/abs/1807.02975v2","url_pdf":"http://arxiv.org/pdf/1807.02975v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"polarimetric-convolutional-network-for-polsar","repo_url":"https://github.com/liuxuvip/Polarimetric-Scattering-Coding","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}