{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:13:44Z","timestamp":1774934024198,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T00:00:00Z","timestamp":1727568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62121001"],"award-info":[{"award-number":["62121001"]}]},{"name":"National Natural Science Foundation of China","award":["U22B2014"],"award-info":[{"award-number":["U22B2014"]}]},{"name":"National Natural Science Foundation of China","award":["2020QNRC001"],"award-info":[{"award-number":["2020QNRC001"]}]},{"name":"National Natural Science Foundation of China","award":["202206960021"],"award-info":[{"award-number":["202206960021"]}]},{"name":"China Association for Science and Technology","award":["62121001"],"award-info":[{"award-number":["62121001"]}]},{"name":"China Association for Science and Technology","award":["U22B2014"],"award-info":[{"award-number":["U22B2014"]}]},{"name":"China Association for Science and Technology","award":["2020QNRC001"],"award-info":[{"award-number":["2020QNRC001"]}]},{"name":"China Association for Science and Technology","award":["202206960021"],"award-info":[{"award-number":["202206960021"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["62121001"],"award-info":[{"award-number":["62121001"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["U22B2014"],"award-info":[{"award-number":["U22B2014"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["2020QNRC001"],"award-info":[{"award-number":["2020QNRC001"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202206960021"],"award-info":[{"award-number":["202206960021"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalanced number between the target and the background spatially and the high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation-learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and spatial\u2013spectral features to improve its representation ability. Firstly, a Graph Convolutional Network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene\u2019s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing the detection and background suppression capabilities. The performance of the proposed approach is evaluated using the average scores of AUCD,F, AUCF,\u03c4, AUCBS, and AUCSNPR. The corresponding values are 0.99660, 0.00078, 0.99587, and 333.629, respectively. These results demonstrate the accuracy of the model in different evaluation metrics, with AUCD,F achieving the highest score, indicating strong detection performance across varying thresholds. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.<\/jats:p>","DOI":"10.3390\/rs16193638","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T05:45:27Z","timestamp":1727675127000},"page":"3638","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Yunsong","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5006-9530","authenticated-orcid":false,"given":"Jiaping","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0335-3878","authenticated-orcid":false,"given":"Weiying","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9576-6337","authenticated-orcid":false,"given":"Paolo","family":"Gamba","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/36.917889","article-title":"Real-Time Processing Algorithms for Target Detection and Classification in Hyperspectral Imagery","volume":"39","author":"Chang","year":"2001","journal-title":"IEEE Trans. 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