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However, they often introduce significant redundant spatial feature information. The octave convolution network is frequently utilized instead of traditional CNN to decrease spatial redundant information of the network and extend its receptive field. However, the 3D octave convolution-based approaches may introduce extensive parameters and complicate the network. To solve these issues, we propose a new HSI classification approach with a multi-scale spectral-spatial network-based framework that combines 2D octave and 3D CNNs. Our method, called MOCNN, first utilizes 2D octave convolution and 3D DenseNet branch networks with various convolutional kernel sizes to obtain complex spatial contextual feature information and spectral characteristics, separately. Moreover, the channel and the spectral attention mechanisms are, respectively, applied to these two branch networks to emphasize significant feature regions and certain important spectral bands that comprise discriminative information for the categorization. Furthermore, a sample balancing strategy is applied to address the sample imbalance problem. Expansive experiments are undertaken on four HSI datasets, demonstrating that our MOCNN approach outperforms several other methods for HSI classification, especially in scenarios dominated by limited and imbalanced sample data.<\/jats:p>","DOI":"10.3390\/rs15071758","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T02:18:27Z","timestamp":1679883507000},"page":"1758","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Multi-Scale Spectral-Spatial Attention Network for Hyperspectral Image Classification Combining 2D Octave and 3D Convolutional Neural Networks"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6958-0443","authenticated-orcid":false,"given":"Lianhui","family":"Liang","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 418002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1454-9665","authenticated-orcid":false,"given":"Shaoquan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1613-9448","authenticated-orcid":false,"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9613-1659","authenticated-orcid":false,"given":"Antonio","family":"Plaza","sequence":"additional","affiliation":[{"name":"Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, E-10071 Caceres, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 418002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TGRS.2017.2765364","article-title":"Recent Advances on Spectral\u2013Spatial Hyperspectral Image Classification: An Overview and New Guidelines","volume":"56","author":"He","year":"2018","journal-title":"IEEE Trans. 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