{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:14:18Z","timestamp":1779200058544,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"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":["41971388"],"award-info":[{"award-number":["41971388"]}],"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":["2022M 723222"],"award-info":[{"award-number":["2022M 723222"]}],"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":["LT2017013"],"award-info":[{"award-number":["LT2017013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China Postdoctoral Science Foundation","award":["41971388"],"award-info":[{"award-number":["41971388"]}]},{"name":"China Postdoctoral Science Foundation","award":["2022M 723222"],"award-info":[{"award-number":["2022M 723222"]}]},{"name":"China Postdoctoral Science Foundation","award":["LT2017013"],"award-info":[{"award-number":["LT2017013"]}]},{"name":"Innovation Team Support Program of Liaoning Higher Education Department","award":["41971388"],"award-info":[{"award-number":["41971388"]}]},{"name":"Innovation Team Support Program of Liaoning Higher Education Department","award":["2022M 723222"],"award-info":[{"award-number":["2022M 723222"]}]},{"name":"Innovation Team Support Program of Liaoning Higher Education Department","award":["LT2017013"],"award-info":[{"award-number":["LT2017013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image (HSI) anomaly detection (HSI-AD) has become a hot issue in hyperspectral information processing as a method for detecting undesired targets without a priori information against unknown background and target information, which can be better adapted to the needs of practical applications. However, the demanding detection environment with no prior and small targets, as well as the large data and high redundancy of HSI itself, make the study of HSI-AD very challenging. First, we propose an HSI-AD method based on the nonsubsampled shearlet transform (NSST) domain spectral information divergence isolation double forest (SI2FM) in this paper. Further, the method excavates the intrinsic deep correlation properties between NSST subband coefficients of HSI in two ways to provide synergistic constraints and guidance on the prediction of abnormal target coefficients. On the one hand, with the \u201cdifference band\u201d as a guide, the global isolation forest and local isolation forest models are constructed based on the spectral information divergence (SID) attribute values of the difference band and the low-frequency and high-frequency subbands, and the anomaly scores are determined by evaluating the path lengths of the isolation binary tree nodes in the forest model to obtain a progressively optimized anomaly detection map. On the other hand, based on the relationship of NSST high-frequency subband coefficients of spatial-spectral dimensions, the three-dimensional forest structure is constructed to realize the co-optimization of multiple anomaly detection maps obtained from the isolation forest. Finally, the guidance of the difference band suppresses the background noise and anomaly interference to a certain extent, enhancing the separability of target and background. The two-branch collaborative optimization based on the NSST subband coefficient correlation mining of HSI enables the prediction of anomaly sample coefficients to be gradually improved from multiple perspectives, which effectively improves the accuracy of anomaly detection. The effectiveness of the algorithm is verified by comparing real hyperspectral datasets captured in four different scenes with eleven typical anomaly detection algorithms currently available.<\/jats:p>","DOI":"10.3390\/rs15030612","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T02:52:21Z","timestamp":1674183141000},"page":"612","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SI2FM: SID Isolation Double Forest Model for Hyperspectral Anomaly Detection"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhenhua","family":"Mu","sequence":"first","affiliation":[{"name":"School of Geography, Liaoning Normal University, Dalian 116029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-5587","authenticated-orcid":false,"given":"Ruoxi","family":"Song","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7600-9939","authenticated-orcid":false,"given":"Xianghai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography, Liaoning Normal University, Dalian 116029, China"},{"name":"School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the Art","volume":"5","author":"Ghamisi","year":"2018","journal-title":"IEEE Geosci. 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