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The target recognition strategies built upon multi-feature have gained favor among researchers due to their ability to provide diverse classification information. The paper introduces a robust multi-feature cross-fusion approach, i.e., a multi-feature dual-stage cross manifold attention network, namely, MF-DCMANet, which essentially relies on the complementary information between different features to enhance the representation ability of targets. In the first-stage process, a Cross-Feature-Network (CFN) module is proposed to mine the middle-level semantic information of monogenic features and polarization features extracted from the PolSAR target. In the second-stage process, a Cross-Manifold-Attention (CMA) transformer is proposed, which takes the input features represented on the Grassmann manifold to mine the nonlinear relationship between features so that rich and fine-grained features can be captured to compute attention weight. Furthermore, a local window is used instead of the global window in the attention mechanism to improve the local feature representation capabilities and reduce the computation. The proposed MF-DCMANet achieves competitive performance on the GOTCHA dataset, with a recognition accuracy of 99.75%. Furthermore, it maintains a high accuracy rate in the few-shot recognition and open-set recognition scenarios, outperforming the current state-of-the-art method by about 2%.<\/jats:p>","DOI":"10.3390\/rs15092292","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T01:28:28Z","timestamp":1682558908000},"page":"2292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["MF-DCMANet: A Multi-Feature Dual-Stage Cross Manifold Attention Network for PolSAR Target Recognition"],"prefix":"10.3390","volume":"15","author":[{"given":"Feng","family":"Li","sequence":"first","affiliation":[{"name":"Radar Research Laboratory, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China"}]},{"given":"Chaoqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Radar Research Laboratory, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2901-2593","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Radar Research Laboratory, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Radar Research Laboratory, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6014","DOI":"10.1109\/ACCESS.2016.2611492","article-title":"Automatic target recognition in synthetic aperture radar imagery: A state-of-the-art review","volume":"4","author":"Gill","year":"2016","journal-title":"IEEE Access"},{"key":"ref_2","first-page":"1","article-title":"Classification of SAR and PolSAR images using deep learning: A review","volume":"2020","author":"Parikh","year":"2020","journal-title":"Int. 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