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However, distinguishing subtle morphological differences among different categories of auroral images presents a significant challenge. To excavate more discriminative information from ground-based auroral images, a novel method named learning representative channel attention information from second-order statistics (LRCAISS) is proposed. The LRCAISS is highlighted with two innovative techniques: a second-order convolutional network and a novel second-order channel attention block. The LRCAISS extends from Resnet50 architecture by incorporating a second-order convolutional network to capture more detailed statistical representation. Meanwhile, the novel second-order channel attention block effectively recalibrates these features. LACAISS is evaluated on two public ground-based auroral image datasets, and the experimental results demonstrate that LRCAISS achieves competitive performance compared to existing methods.<\/jats:p>","DOI":"10.3390\/rs16173178","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T05:34:49Z","timestamp":1724823289000},"page":"3178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Auroral Image Classification Based on Second-Order Convolutional Network and Channel Attention Awareness"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0358-1587","authenticated-orcid":false,"given":"Yangfan","family":"Hu","sequence":"first","affiliation":[{"name":"College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China"},{"name":"High Impact Weather Key Laboratory of CMA, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9264-0608","authenticated-orcid":false,"given":"Zeming","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China"},{"name":"High Impact Weather Key Laboratory of CMA, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0065-5264","authenticated-orcid":false,"given":"Pinglv","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9323-1059","authenticated-orcid":false,"given":"Xiaofeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China"},{"name":"High Impact Weather Key Laboratory of CMA, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9530-4925","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China"},{"name":"High Impact Weather Key Laboratory of CMA, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Teaching and Research Support Center, Army Engineering University of PLA, Nanjing 210014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"ref_1","unstructured":"Syrjaesuo, M.T., Donovan, E.F., Cogger, L.L., Developmentiasted, S.T.F., and Forumwmsf, M.S. 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