{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T08:06:36Z","timestamp":1768032396324,"version":"3.49.0"},"reference-count":64,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42271409"],"award-info":[{"award-number":["42271409"]}]},{"name":"National Natural Science Foundation of China","award":["62071084"],"award-info":[{"award-number":["62071084"]}]},{"name":"National Natural Science Foundation of China","award":["LH2021D022"],"award-info":[{"award-number":["LH2021D022"]}]},{"name":"National Natural Science Foundation of China","award":["135509136"],"award-info":[{"award-number":["135509136"]}]},{"name":"Heilongjiang Science Foundation Project of China","award":["42271409"],"award-info":[{"award-number":["42271409"]}]},{"name":"Heilongjiang Science Foundation Project of China","award":["62071084"],"award-info":[{"award-number":["62071084"]}]},{"name":"Heilongjiang Science Foundation Project of China","award":["LH2021D022"],"award-info":[{"award-number":["LH2021D022"]}]},{"name":"Heilongjiang Science Foundation Project of China","award":["135509136"],"award-info":[{"award-number":["135509136"]}]},{"name":"Fundamental Research Funds in Heilongjiang Provincial Universities of China","award":["42271409"],"award-info":[{"award-number":["42271409"]}]},{"name":"Fundamental Research Funds in Heilongjiang Provincial Universities of China","award":["62071084"],"award-info":[{"award-number":["62071084"]}]},{"name":"Fundamental Research Funds in Heilongjiang Provincial Universities of China","award":["LH2021D022"],"award-info":[{"award-number":["LH2021D022"]}]},{"name":"Fundamental Research Funds in Heilongjiang Provincial Universities of China","award":["135509136"],"award-info":[{"award-number":["135509136"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks are widely used in the field of hyperspectral image classification. After continuous exploration and research in recent years, convolutional neural networks have achieved good classification performance in the field of hyperspectral image classification. However, we have to face two main challenges that restrict the improvement of hyperspectral classification accuracy, namely, the high dimension of hyperspectral images and the small number of training samples. In order to solve these problems, in this paper, a new hyperspectral classification method is proposed. First, a three-dimensional octave convolution (3D-OCONV) is proposed. Subsequently, a dense connection structure of three-dimensional asymmetric convolution (DC-TAC) is designed. In the spectral branch, the spectral features are extracted through a combination of the 3D-OCONV and spectral attention modules, followed by the DC-TAC. In the spatial branch, a three-dimensional, multiscale spatial attention module (3D-MSSAM) is presented. The spatial information is fully extracted using the 3D-OCONV, 3D-MSSAM, and DC-TAC. Finally, the spectral and spatial information extracted from the two branches is fully fused with an interactive information fusion module. Compared to some state-of-the-art classification methods, the proposed method shows superior classification performance with a small number of training samples on four public datasets.<\/jats:p>","DOI":"10.3390\/rs15010257","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:00:59Z","timestamp":1672628459000},"page":"257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Hyperspectral Image Classification Based on a 3D Octave Convolution and 3D Multiscale Spatial Attention Network"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5877-1762","authenticated-orcid":false,"given":"Cuiping","family":"Shi","sequence":"first","affiliation":[{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}]},{"given":"Jingwei","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}]},{"given":"Tianyi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Physical Education, Qiqihar University, Qiqihar 161000, China"}]},{"given":"Liguo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Dalian Nationalities University, Dalian 116000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4481","DOI":"10.1109\/TIM.2018.2887069","article-title":"Medical Hyperspectral Image Classification Based on End-to-End Fusion Deep Neural Network","volume":"68","author":"Wei","year":"2019","journal-title":"IEEE Trans. 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