{"id":"https://openalex.org/W4415594430","doi":"https://doi.org/10.1109/tgrs.2025.3625232","title":"DPMN: Deep Prior Mamba Network for Hyperspectral Anomaly Detection","display_name":"DPMN: Deep Prior Mamba Network for Hyperspectral Anomaly Detection","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415594430","doi":"https://doi.org/10.1109/tgrs.2025.3625232"},"language":null,"primary_location":{"id":"doi:10.1109/tgrs.2025.3625232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3625232","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Linwei Li","orcid":"https://orcid.org/0009-0000-4688-4145"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linwei Li","raw_affiliation_strings":["Key Laboratory for Information Science of Electromagnetic Waves (MoE) and the Image and Intelligence Laboratory, School of Information Science and Technology, Fudan University, Shanghai, China","Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0000-4688-4145","affiliations":[{"raw_affiliation_string":"Key Laboratory for Information Science of Electromagnetic Waves (MoE) and the Image and Intelligence Laboratory, School of Information Science and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059575276","display_name":"Bin Wang","orcid":"https://orcid.org/0000-0003-4748-6426"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Wang","raw_affiliation_strings":["Key Laboratory for Information Science of Electromagnetic Waves (MoE) and the Image and Intelligence Laboratory, School of Information Science and Technology, Fudan University, Shanghai, China","Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-4748-6426","affiliations":[{"raw_affiliation_string":"Key Laboratory for Information Science of Electromagnetic Waves (MoE) and the Image and Intelligence Laboratory, School of Information Science and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":1.5239,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.86256109,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9447000026702881,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9447000026702881,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7421000003814697},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5978999733924866},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.583299994468689},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5605999827384949},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5309000015258789},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4510999917984009},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4341999888420105},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4250999987125397},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.3709000051021576}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7421000003814697},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7300999760627747},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6557000279426575},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5978999733924866},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.583299994468689},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5605999827384949},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4510999917984009},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4341999888420105},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4250999987125397},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.3255000114440918},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26980000734329224},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.26170000433921814},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2581999897956848}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3625232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3625232","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5031566633","display_name":null,"funder_award_id":"2022YFB3903404","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6469727287","display_name":null,"funder_award_id":"2022YFB3903404","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8249770577","display_name":null,"funder_award_id":"62371140","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1991190032","https://openalex.org/W2004491663","https://openalex.org/W2037034832","https://openalex.org/W2040078680","https://openalex.org/W2047870694","https://openalex.org/W2124463804","https://openalex.org/W2145962650","https://openalex.org/W2163129097","https://openalex.org/W2165835468","https://openalex.org/W2183325870","https://openalex.org/W2288752886","https://openalex.org/W2288987301","https://openalex.org/W2295576075","https://openalex.org/W2497075055","https://openalex.org/W2507498837","https://openalex.org/W2549107715","https://openalex.org/W2586973374","https://openalex.org/W2590856740","https://openalex.org/W2740976805","https://openalex.org/W2789345570","https://openalex.org/W2887985146","https://openalex.org/W2898121906","https://openalex.org/W2951401720","https://openalex.org/W2972480129","https://openalex.org/W3038851053","https://openalex.org/W3137199127","https://openalex.org/W3151666947","https://openalex.org/W3168931281","https://openalex.org/W3186256209","https://openalex.org/W3195044646","https://openalex.org/W3199351457","https://openalex.org/W3205581890","https://openalex.org/W4214806231","https://openalex.org/W4220831207","https://openalex.org/W4225850527","https://openalex.org/W4226075372","https://openalex.org/W4282929851","https://openalex.org/W4285233009","https://openalex.org/W4296210064","https://openalex.org/W4312572145","https://openalex.org/W4313590927","https://openalex.org/W4319990014","https://openalex.org/W4321380750","https://openalex.org/W4376457061","https://openalex.org/W4376607587","https://openalex.org/W4386849902","https://openalex.org/W4387331383","https://openalex.org/W4388240218","https://openalex.org/W4390347709","https://openalex.org/W4391467940","https://openalex.org/W4391547538","https://openalex.org/W4391594007","https://openalex.org/W4391759484","https://openalex.org/W4391956372","https://openalex.org/W4394007483","https://openalex.org/W4394805276","https://openalex.org/W4398151306","https://openalex.org/W4399039779","https://openalex.org/W4400810737","https://openalex.org/W4401705603","https://openalex.org/W4402402045"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,44],"hyperspectral":[3,187],"anomaly":[4],"detection":[5],"(HAD)":[6],"utilizing":[7],"deep":[8,80,102],"learning":[9,81],"have":[10],"garnered":[11],"significant":[12],"attention":[13],"due":[14],"to":[15,131,138,147,156,180],"their":[16],"superior":[17],"performance.":[18],"However,":[19],"most":[20],"existing":[21],"methods":[22],"based":[23],"on":[24,31,176,225],"convolutional":[25],"neural":[26],"networks":[27],"and":[28,34,38,58,90,120,135,214],"Transformer":[29],"focus":[30],"extracting":[32],"local":[33,133,146],"global":[35],"features":[36],"separately":[37],"assume":[39],"that":[40,232],"the":[41,52,55,60,71,76,79,86,92,115,121,158,172,183,195,199,204,219],"background":[42,57,122,154,159,167,177],"resides":[43],"a":[45,101,152,191],"single":[46],"subspace":[47],"for":[48,107,163],"reconstruction,":[49,164],"thereby":[50],"reducing":[51],"quality":[53],"of":[54,62,78,88,112,174,186,221],"reconstructed":[56],"decreasing":[59],"accuracy":[61],"HAD.":[63],"Moreover,":[64],"although":[65],"incorporating":[66],"prior":[67,103],"physical":[68],"knowledge":[69],"into":[70,160,194],"loss":[72,196],"function":[73],"can":[74],"enhance":[75],"performance":[77],"networks,":[82],"it":[83],"also":[84,217],"increases":[85],"number":[87,220],"hyperparameters":[89],"complicates":[91],"tuning":[93],"process.":[94],"To":[95],"address":[96],"these":[97],"issues,":[98],"we":[99,189],"propose":[100],"Mamba":[104,137],"network":[105],"(DPMN)":[106],"HAD,":[108],"which":[109,208],"primarily":[110],"consists":[111],"two":[113],"components:":[114],"abundance":[116],"generation":[117],"module":[118,124],"(AGM)":[119],"reconstruction":[123,168],"(BRM).":[125],"Specifically,":[126],"AGM":[127],"employs":[128],"convolution":[129],"layers":[130],"extract":[132],"information":[134],"introduces":[136],"capture":[139],"long-range":[140],"dependencies,":[141],"achieving":[142],"feature":[143],"extraction":[144],"from":[145],"global.":[148],"Subsequently,":[149],"BRM":[150],"utilizes":[151],"learnable":[153],"dictionary":[155],"divide":[157],"multiple":[161],"subspaces":[162],"realizing":[165],"accurate":[166],"while":[169],"effectively":[170],"suppressing":[171],"interference":[173],"anomalies":[175],"reconstruction.":[178],"Furthermore,":[179],"fully":[181],"leverage":[182],"intrinsic":[184],"properties":[185],"images,":[188],"incorporate":[190],"regularization":[192],"term":[193],"function,":[197],"merging":[198],"total":[200],"variation":[201],"(TV)":[202],"with":[203],"low-rank":[205],"representation":[206],"(LRR),":[207],"not":[209],"only":[210],"exploits":[211],"spatial":[212],"smoothness":[213],"low-rankness":[215],"but":[216],"reduces":[218],"hyperparameters.":[222],"Experimental":[223],"results":[224],"eight":[226],"publicly":[227],"available":[228,245],"real":[229],"datasets":[230],"demonstrate":[231],"our":[233,242],"method":[234],"significantly":[235],"outperforms":[236],"other":[237],"state-of-the-art":[238],"methods.":[239],"In":[240],"addition,":[241],"code":[243],"is":[244],"at:":[246],"https://github.com/I3ab/DPMN.":[247]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2025-11-07T23:20:04.922697","created_date":"2025-10-28T00:00:00"}