{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T16:01:17Z","timestamp":1792684877087,"version":"4.5.0"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013314","name":"Higher Education Discipline Innovation Project","doi-asserted-by":"publisher","award":["B17035"],"award-info":[{"award-number":["B17035"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi Province","doi-asserted-by":"publisher","award":["2025JC-YBQN-875"],"award-info":[{"award-number":["2025JC-YBQN-875"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP23K16911"],"award-info":[{"award-number":["JP23K16911"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019024","name":"Guangdong Polytechnic Normal University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.114647","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T23:23:22Z","timestamp":1786663402000},"page":"114647","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"PF","title":["TBCNet: Twin-branch collaborative network for hyperspectral anomaly detection"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2439-6815","authenticated-orcid":false,"given":"Dong","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3743-5131","authenticated-orcid":false,"given":"Mingtao","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1895-1894","authenticated-orcid":false,"given":"Pei","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianling","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0898-7802","authenticated-orcid":false,"given":"Yuta","family":"Asano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0269-5649","authenticated-orcid":false,"given":"Xin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2083-4438","authenticated-orcid":false,"given":"Chih-Chung","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5397-5221","authenticated-orcid":false,"given":"Huixin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6116-3194","authenticated-orcid":false,"given":"Jinchang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.114647_b1","first-page":"1","article-title":"HADGSM: A unified nonconvex framework for hyperspectral anomaly detection","volume":"62","author":"Ren","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"16","key":"10.1016\/j.patcog.2026.114647_b2","doi-asserted-by":"crossref","first-page":"3036","DOI":"10.3390\/rs16163036","article-title":"A multi-scale mask convolution-based blind-spot network for hyperspectral anomaly detection","volume":"16","author":"Yang","year":"2024","journal-title":"Remote. Sens."},{"key":"10.1016\/j.patcog.2026.114647_b3","doi-asserted-by":"crossref","first-page":"6981","DOI":"10.1109\/JSTARS.2025.3542457","article-title":"Nonlocal and local feature-coupled self-supervised network for hyperspectral anomaly detection","volume":"18","author":"Wang","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"10.1016\/j.patcog.2026.114647_b4","first-page":"1","article-title":"Anomaly detection of hyperspectral image with hierarchical antinoise mutual-incoherence-induced low-rank representation","volume":"61","author":"Guo","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b5","first-page":"1","article-title":"KACNet: Kolmogorov\u2013Arnold convolution network for hyperspectral anomaly detection","volume":"63","author":"Wu","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.patcog.2026.114647_b6","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MGRS.2021.3105440","article-title":"Hyperspectral Anomaly Detection: A survey","volume":"10","author":"Su","year":"2022","journal-title":"IEEE Geosci. Remote. Sens. Mag."},{"issue":"10","key":"10.1016\/j.patcog.2026.114647_b7","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/29.60107","article-title":"Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution","volume":"38","author":"Reed","year":"1990","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"10.1016\/j.patcog.2026.114647_b8","first-page":"1","article-title":"Information entropy estimation based on point-set topology for hyperspectral anomaly detection","volume":"62","author":"Sun","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"10.1016\/j.patcog.2026.114647_b9","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/TGRS.2014.2343955","article-title":"Collaborative representation for hyperspectral anomaly detection","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.patcog.2026.114647_b10","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TGRS.2019.2936609","article-title":"Graph and total variation regularized low-rank representation for hyperspectral anomaly detection","volume":"58","author":"Cheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"11","key":"10.1016\/j.patcog.2026.114647_b11","doi-asserted-by":"crossref","first-page":"9553","DOI":"10.1109\/TGRS.2021.3049224","article-title":"Hyperspectral anomaly detection via deep plug-and-play denoising CNN regularization","volume":"59","author":"Fu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b12","doi-asserted-by":"crossref","first-page":"6714","DOI":"10.1109\/TIP.2025.3620091","article-title":"Self-supervised masked graph autoencoder for hyperspectral anomaly detection","volume":"34","author":"Tu","year":"2025","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"10.1016\/j.patcog.2026.114647_b13","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/TNNLS.2025.3634765","article-title":"Hyperspectral anomaly detection via hybrid convolutional and transformer-based U-Net with error attention mechanism","volume":"37","author":"Wang","year":"2026","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2026.114647_b14","first-page":"1","article-title":"Adaptive dual-domain learning for hyperspectral anomaly detection with state-space models","volume":"63","author":"Liu","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b15","article-title":"Multi-scale frequency-guided two-stream network for hyperspectral anomaly detection","volume":"136","author":"Zhao","year":"2025","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.patcog.2026.114647_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2026.113967","article-title":"DFBSNet: Dual frequency-domain branch fusion and selection network for hyperspectral anomaly detection","volume":"180","author":"Yao","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114647_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2026.3676658","article-title":"SM-HAD: Spectrum mamba for hyperspectral anomaly detection","volume":"64","author":"Rachamalla","year":"2026","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b18","first-page":"1","article-title":"Matrix factorization with framelet and saliency priors for hyperspectral anomaly detection","volume":"61","author":"Shen","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"6","key":"10.1016\/j.patcog.2026.114647_b19","doi-asserted-by":"crossref","first-page":"8358","DOI":"10.1109\/TNNLS.2022.3227167","article-title":"Hyperspectral anomaly detection using reconstruction fusion of quaternion frequency domain analysis","volume":"35","author":"Tu","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2026.114647_b20","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.isprsjprs.2016.10.006","article-title":"A robust background regression based score estimation algorithm for hyperspectral anomaly detection","volume":"122","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112535","article-title":"HADDNLP: Hyperspectral anomaly detection via double nonlocal priors","volume":"172","author":"Ren","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114647_b22","first-page":"1","article-title":"Generalized nonconvex low-rank tensor representation for hyperspectral anomaly detection","volume":"61","author":"Qin","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b23","first-page":"1","article-title":"Continuous tensor representation for hyperspectral anomaly detection","volume":"63","author":"Zeng","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.patcog.2026.114647_b24","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TCYB.2022.3175771","article-title":"Learning tensor low-rank representation for hyperspectral anomaly detection","volume":"53","author":"Wang","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.patcog.2026.114647_b25","first-page":"1","article-title":"Hyperspectral anomaly detection with guided autoencoder","volume":"60","author":"Xiang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3476285","article-title":"Memory-augmented autoencoder with adaptive reconstruction and sample attribution mining for hyperspectral anomaly detection","volume":"62","author":"Huo","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b27","first-page":"1","article-title":"BockNet: Blind-block reconstruction network with a guard window for hyperspectral anomaly detection","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"10.1016\/j.patcog.2026.114647_b28","doi-asserted-by":"crossref","first-page":"717","DOI":"10.3390\/rs16040717","article-title":"A novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency for hyperspectral anomaly detection","volume":"16","author":"Zhao","year":"2024","journal-title":"Remote. Sens."},{"key":"10.1016\/j.patcog.2026.114647_b29","first-page":"1","article-title":"Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"10.1016\/j.patcog.2026.114647_b30","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1049\/cit2.12154","article-title":"Frequency-to-spectrum mapping GAN for semisupervised hyperspectral anomaly detection","volume":"8","author":"Wang","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"10.1016\/j.patcog.2026.114647_b31","series-title":"Proc. IEEE\/CVF Winter Conf. Appl. Comput. Vis.","first-page":"5623","article-title":"DMS2F-HAD: A dual-branch mamba-based spatial\u2013spectral fusion network for hyperspectral anomaly detection","author":"Pant","year":"2026"},{"key":"10.1016\/j.patcog.2026.114647_b32","first-page":"1","article-title":"PDBSNet: Pixel-shuffle downsampling blind-spot reconstruction network for hyperspectral anomaly detection","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b33","first-page":"1","article-title":"BS3LNet: A new blind-spot self-supervised learning network for hyperspectral anomaly detection","volume":"61","author":"Gao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b34","article-title":"Dual-stream background modeling network with anomaly suppression for hyperspectral anomaly detection","volume":"148","author":"Huo","year":"2026","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.patcog.2026.114647_b35","first-page":"1","article-title":"MMR-HAD: Multiscale mamba reconstruction network for hyperspectral anomaly detection","volume":"63","author":"Fu","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b36","first-page":"1","article-title":"Sliding dual-window-inspired reconstruction network for hyperspectral anomaly detection","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"10.1016\/j.patcog.2026.114647_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104494","article-title":"Multi-scale memory network with separation training for hyperspectral anomaly detection","volume":"63","author":"Huo","year":"2026","journal-title":"Inf. Process. Manag."},{"issue":"20","key":"10.1016\/j.patcog.2026.114647_b38","doi-asserted-by":"crossref","first-page":"33847","DOI":"10.1109\/JSEN.2024.3455258","article-title":"Hyperspectral anomaly detection based on empirical mode decomposition and local weighted contrast","volume":"24","author":"Zhao","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.patcog.2026.114647_b39","first-page":"1","article-title":"Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations","volume":"60","author":"Zhuang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2026.106019","article-title":"DSFNet: Dual-stream spatial-spectral fusion network for hyperspectral anomaly detection","volume":"175","author":"Wu","year":"2026","journal-title":"Digit. Signal Process."},{"issue":"6","key":"10.1016\/j.patcog.2026.114647_b41","doi-asserted-by":"crossref","first-page":"5131","DOI":"10.1109\/TGRS.2020.3021671","article-title":"An effective evaluation tool for hyperspectral target detection: 3D receiver operating characteristic curve analysis","volume":"59","author":"Chang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114647_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.optlaseng.2026.109657","article-title":"S3CRAD: Superpixel-guided background inpainting and spatial-spectral constrained representation for hyperspectral anomaly detection","volume":"201","author":"You","year":"2026","journal-title":"Opt. Lasers Eng."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326016110?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326016110?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T14:00:22Z","timestamp":1788444022000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326016110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":42,"alternative-id":["S0031320326016110"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114647","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"TBCNet: Twin-branch collaborative network for hyperspectral anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114647","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114647"}}