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However, the current CNN-based classifiers follow patch-based frameworks, which need input images to be divided into overlapping patches. Consequently, these classification approaches have the drawback of requiring repeated calculations and only relying on local information. In addition, the receptive field size in conventional CNN-based methods is fixed, which limits the potential to extract features. In this paper, a hybrid attention-based encoder\u2013decoder fully convolutional network (HA-EDNet) is presented for PolSAR classification. Unlike traditional CNN-based approaches, the encoder\u2013decoder fully convolutional network (EDNet) can use an arbitrary-size image as input without dividing. Then, the output is the whole image classification result. Meanwhile, the self-attention module is used to establish global spatial dependence and extract context characteristics, which can improve the performance of classification. Moreover, an attention-based selective kernel module (SK module) is included in the network. In the module, softmax attention is employed to fuse several branches with different receptive field sizes. Consequently, the module can capture features with different scales and further boost classification accuracy. The experiment results demonstrate that the HA-EDNet achieves superior performance compared to CNN-based and traditional fully convolutional network methods.<\/jats:p>","DOI":"10.3390\/rs15020526","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T05:30:07Z","timestamp":1673847007000},"page":"526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Hybrid Attention-Based Encoder\u2013Decoder Fully Convolutional Network for PolSAR Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5553-1747","authenticated-orcid":false,"given":"Zheng","family":"Fang","sequence":"first","affiliation":[{"name":"Key Lab of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China"}]},{"given":"Gong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Lab of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China"},{"name":"Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute, Shenzhen 518000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5317-0730","authenticated-orcid":false,"given":"Qijun","family":"Dai","sequence":"additional","affiliation":[{"name":"Key Lab of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China"}]},{"given":"Biao","family":"Xue","sequence":"additional","affiliation":[{"name":"Key Lab of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Lab of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China"},{"name":"Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China"},{"name":"Fujian Provincial Key Lab of Coastal Basin Environment, Fujian Polytechnic Normal University, Fuqing 350300, China"},{"name":"FKey Laboratory of Southeast Coast Marine Information Intelligent Perception and Application, Ministry of Natural Resources, Zhangzhou Institute of Surveying and Mapping, Zhangzhou 363001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112498","DOI":"10.1016\/j.rse.2021.112498","article-title":"Reflection of and vision for the decomposition algorithm development and application in earth observation studies using PolSAR technique and data","volume":"261","author":"Duan","year":"2021","journal-title":"Remote Sens. 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