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In this paper, we design a dual multi-head contextual self-attention (DMuCA) network for HSI classification with the fewest possible parameters and lower computation costs. To effectively capture rich contextual dependencies from both domains, we decouple the spatial and spectral contextual attention into two sub-blocks, SaMCA and SeMCA, where depth-wise convolution is employed to contextualize the input keys in the pure dimension. Thereafter, multi-head local attentions are implemented as group processing when the keys are alternately concatenated with the queries. In particular, in the SeMCA block, we group the spatial pixels by evenly sampling and create multi-head channel attention on each sampling set, to reduce the number of the training parameters and avoid the storage increase. In addition, the static contextual keys are fused with the dynamic attentional features in each block to strengthen the capacity of the model in data representation. Finally, the decoupled sub-blocks are weighted and summed together for 3-D attention perception of HSI. The DMuCA module is then plugged into a ResNet to perform HSI classification. Extensive experiments demonstrate that our proposed DMuCA achieves excellent results over several state-of-the-art attention mechanisms with the same backbone.<\/jats:p>","DOI":"10.3390\/rs14133091","type":"journal-article","created":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:07:02Z","timestamp":1656374822000},"page":"3091","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Dual Multi-Head Contextual Attention Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4289-7114","authenticated-orcid":false,"given":"Miaomiao","family":"Liang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"He","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6206-450X","authenticated-orcid":false,"given":"Xiangchun","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2364-2749","authenticated-orcid":false,"given":"Zhe","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Telecommunication and Information Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep learning for hyperspectral image classification: An overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. 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