{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:02:41Z","timestamp":1760598161006,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T00:00:00Z","timestamp":1654560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research Project of Zhejiang Lab","award":["No.2021MH0AC01"],"award-info":[{"award-number":["No.2021MH0AC01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Since anomaly targets in hyperspectral images (HSIs) with high spatial resolution appear as connected areas instead of single pixels or subpixels, both spatial and spectral information of HSIs can be exploited for a hyperspectal anomaly detection (AD) task. This article proposes a hyperspectral AD method based on Wasserstein distance (WD) and spatial filtering (called AD-WDSF). Based on the assumption that both background and anomaly targets obey the multivariate Gaussian distribution, background and anomaly target distributions are estimated in the local regions of HSIs. Subsequently, the anomaly intensity of test pixels centered in the local regions are determined via measuring the WD between background and anomaly target distributions. Lastly, spatial filters, i.e., guided filter (GF), total variation curvature filter (TVCF), and Maxtree filter, are exploited to further refine detection results. Experimental results conducted on two real hyperspectral data sets demonstrate that the proposed method achieves competitive detection performance compared with the state-of-the-art AD methods.<\/jats:p>","DOI":"10.3390\/rs14122730","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2730","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Hyperspectral Anomaly Detection Based on Wasserstein Distance and Spatial Filtering"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0588-8793","authenticated-orcid":false,"given":"Xiaoyu","family":"Cheng","sequence":"first","affiliation":[{"name":"Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310000, China"},{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4559-9734","authenticated-orcid":false,"given":"Maoxing","family":"Wen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]},{"given":"Cong","family":"Gao","sequence":"additional","affiliation":[{"name":"Senior Research Officer School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK"}]},{"given":"Yueming","family":"Wang","sequence":"additional","affiliation":[{"name":"Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310000, China"},{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou 311100, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Freitas, S., Silva, H., and Silva, E. 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