{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T19:12:10Z","timestamp":1773256330046,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T00:00:00Z","timestamp":1682726400000},"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":["42271409"],"award-info":[{"award-number":["42271409"]}],"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":["62071084"],"award-info":[{"award-number":["62071084"]}],"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":["LH2021D022"],"award-info":[{"award-number":["LH2021D022"]}],"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":["145209149"],"award-info":[{"award-number":["145209149"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"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":["145209149"],"award-info":[{"award-number":["145209149"]}]},{"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":["145209149"],"award-info":[{"award-number":["145209149"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image classification (HSIC) is one of the most important research topics in the field of remote sensing. However, it is difficult to label hyperspectral data, which limits the improvement of classification performance of hyperspectral images in the case of small samples. To alleviate this problem, in this paper, a dual-branch network which combines cross-channel dense connection and multi-scale dual aggregated attention (CDC_MDAA) is proposed. On the spatial branch, a cross-channel dense connections (CDC) module is designed. The CDC can effectively combine cross-channel convolution with dense connections to extract the deep spatial features of HSIs. Then, a spatial multi-scale dual aggregated attention module (SPA_MDAA) is constructed. The SPA_MDAA adopts dual autocorrelation for attention modeling to strengthen the differences between features and enhance the ability to pay attention to important features. On the spectral branch, a spectral multi-scale dual aggregated attention module (SPE_MDAA) is designed to capture important spectral features. Finally, the spatial spectral features are fused, and the classification results are obtained. The experimental results show that the classification performance of the proposed method is superior to some state-of-the-art methods in small samples and has good generalization.<\/jats:p>","DOI":"10.3390\/rs15092367","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:10:03Z","timestamp":1682943003000},"page":"2367","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Cross-Channel Dense Connection and Multi-Scale Dual Aggregated Attention Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0157-9926","authenticated-orcid":false,"given":"Haiyang","family":"Wu","sequence":"first","affiliation":[{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5877-1762","authenticated-orcid":false,"given":"Cuiping","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liguo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Dalian Nationalities University, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhan","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. 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