{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:04:31Z","timestamp":1781647471527,"version":"3.54.5"},"reference-count":68,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFF1300201"],"award-info":[{"award-number":["2022YFF1300201"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFD2000102"],"award-info":[{"award-number":["2021YFD2000102"]}]},{"name":"National Key Research and Development Program of China","award":["42101380"],"award-info":[{"award-number":["42101380"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFF1300201"],"award-info":[{"award-number":["2022YFF1300201"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFD2000102"],"award-info":[{"award-number":["2021YFD2000102"]}]},{"name":"National Natural Science Foundation of China","award":["42101380"],"award-info":[{"award-number":["42101380"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis. Latterly, significant attention has been given to HAD methods based on Autoencoders (AE). Nevertheless, due to a lack of prior information, transferring of modeling capacity, and the \u201ccurse of dimensionality\u201d, AE-based detectors still have limited performance. To address the drawbacks, we propose a Multi-Prior Graph Autoencoder (MPGAE) with ranking-based band selection for HAD. There are three main components: the ranking-based band selection component, the adaptive salient weight component, and the graph autoencoder. First, the ranking-based band selection component removes redundant spectral channels by ranking the bands by employing piecewise-smooth first. Then, the adaptive salient weight component adjusts the reconstruction ability of the AE based on the salient prior, by calculating spectral-spatial features of the local context and the multivariate normal distribution of backgrounds. Finally, to preserve the geometric structure in the latent space, the graph autoencoder detects anomalies by obtaining reconstruction errors with a superpixel segmentation-based graph regularization. In particular, the loss function utilizes \u21132,1-norm and adaptive salient weight to enhance the capacity of modeling anomaly patterns. Experimental results demonstrate that the proposed MPGAE effectively outperforms other state-of-the-art HAD detectors.<\/jats:p>","DOI":"10.3390\/rs15184430","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T09:09:21Z","timestamp":1694423361000},"page":"4430","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5601-7838","authenticated-orcid":false,"given":"Nan","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology CAS, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710100, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuetian","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology CAS, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710100, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0914-8170","authenticated-orcid":false,"given":"Haiwei","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology CAS, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710100, China"},{"name":"Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing, Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology CAS, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710100, China"},{"name":"Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing, Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology CAS, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710100, China"},{"name":"Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing, Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuebin","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology CAS, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710100, China"},{"name":"Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing, Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","article-title":"Imaging spectrometry for earth remote sensing","volume":"228","author":"Goetz","year":"1985","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/TIP.2022.3226418","article-title":"Transition is a process: Pair-to-video change detection networks for very high resolution remote sensing images","volume":"32","author":"Lin","year":"2022","journal-title":"IEEE Trans. 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