{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:22:19Z","timestamp":1772932939611,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62077038"],"award-info":[{"award-number":["62077038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672405"],"award-info":[{"award-number":["61672405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks (CNNs) can extract advanced features of joint spectral\u2013spatial information, which are useful for hyperspectral image (HSI) classification. However, the patch-based neighborhoods of samples with fixed sizes are usually used as the input of the CNNs, which cannot dig out the homogeneousness between the pixels within and outside of the patch. In addition, the spatial features are quite different in different spectral bands, which are not fully utilized by the existing methods. In this paper, a two-branch convolutional neural network based on multi-spectral entropy rate superpixel segmentation (TBN-MERS) is designed for HSI classification. Firstly, entropy rate superpixel (ERS) segmentation is performed on the image of each spectral band in an HSI, respectively. The segmented images obtained are stacked band by band, called multi-spectral entropy rate superpixel segmentation image (MERSI), and then preprocessed to serve as the input of one branch in TBN-MERS. The preprocessed HSI is used as the input of the other branch in TBN-MERS. TBN-MERS extracts features from both the HSI and the MERSI and then utilizes the fused spectral\u2013spatial features for the classification of HSIs. TBN-MERS makes full use of the joint spectral\u2013spatial information of HSIs at the scale of superpixels and the scale of neighborhood. Therefore, it achieves excellent performance in the classification of HSIs. Experimental results on four datasets demonstrate that the proposed TBN-MERS can effectively extract features from HSIs and significantly outperforms some state-of-the-art methods with a few training samples.<\/jats:p>","DOI":"10.3390\/rs14071569","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T23:31:43Z","timestamp":1648164703000},"page":"1569","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Two-Branch Convolutional Neural Network Based on Multi-Spectral Entropy Rate Superpixel Segmentation for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Caihong","family":"Mu","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Collaborative Innovation Center of Quantum Information of Shaanxi Province, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9796-2518","authenticated-orcid":false,"given":"Zhidong","family":"Dong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Collaborative Innovation Center of Quantum Information of Shaanxi Province, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-0731","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. 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