{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:39:54Z","timestamp":1763105994470,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T00:00:00Z","timestamp":1709337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2022JQ-631","2022GY-080","62376209"],"award-info":[{"award-number":["2022JQ-631","2022GY-080","62376209"]}]},{"name":"Key Research and Development Project of Shaanxi Province","award":["2022JQ-631","2022GY-080","62376209"],"award-info":[{"award-number":["2022JQ-631","2022GY-080","62376209"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022JQ-631","2022GY-080","62376209"],"award-info":[{"award-number":["2022JQ-631","2022GY-080","62376209"]}],"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>The classifiers based on the convolutional neural network (CNN) and graph convolutional network (GCN) have demonstrated their effectiveness in hyperspectral image (HSI) classification. However, their performance is limited by the high time complexity of CNN, spatial complexity of GCN, and insufficient labeled samples. To ease these limitations, the spectral\u2013spatial graph convolutional network with dynamic-synchronized multiscale features is proposed for few-shot HSI classification. Firstly, multiscale patches are generated to enrich training samples in the feature space. A weighted spectral optimization module is explored to evaluate the discriminate information among different bands of patches. Then, the adaptive dynamic graph convolutional module is proposed to extract local and long-range spatial\u2013spectral features of patches at each scale. Considering that features of different scales can be regarded as sequential data due to intrinsic correlations, the bidirectional LSTM is adopted to synchronously extract the spectral\u2013spatial characteristics from all scales. Finally, auxiliary classifiers are utilized to predict labels of samples at each scale and enhance the training stability. Label smoothing is introduced into the classification loss to reduce the influence of misclassified samples and imbalance of classes. Extensive experiments demonstrate the superiority of the proposed method over other state-of-the-art methods, obtaining overall accuracies of 87.25%, 92.72%, and 93.36% on the Indian Pines, Pavia University, and Salinas datasets, respectively.<\/jats:p>","DOI":"10.3390\/rs16050895","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T10:11:57Z","timestamp":1709547117000},"page":"895","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Spectral\u2013Spatial Graph Convolutional Network with Dynamic-Synchronized Multiscale Features for Few-Shot Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0327-6729","authenticated-orcid":false,"given":"Shuai","family":"Liu","sequence":"first","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongfei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengji","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8032-7542","authenticated-orcid":false,"given":"Jie","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.isprsjprs.2023.05.021","article-title":"Dynamic estimation of rice aboveground biomass based on spectral and spatial information extracted from hyperspectral remote sensing images at different combinations of growth stages","volume":"202","author":"Xu","year":"2023","journal-title":"ISPRS J. 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