{"id":"https://openalex.org/W4416922363","doi":"https://doi.org/10.1109/tgrs.2025.3639448","title":"Cross-Scene Hyperspectral Image Classification Network With Dynamic Perturbation and Self-Knowledge Distillation","display_name":"Cross-Scene Hyperspectral Image Classification Network With Dynamic Perturbation and Self-Knowledge Distillation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416922363","doi":"https://doi.org/10.1109/tgrs.2025.3639448"},"language":null,"primary_location":{"id":"doi:10.1109/tgrs.2025.3639448","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3639448","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":"Yuhang Hong","orcid":"https://orcid.org/0009-0007-7105-7126"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhang Hong","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0009-0007-7105-7126","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053487344","display_name":"Zhixi Feng","orcid":"https://orcid.org/0000-0002-7372-9180"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhixi Feng","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-7372-9180","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100764373","display_name":"Shuyuan Yang","orcid":"https://orcid.org/0000-0002-4796-5737"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuyuan Yang","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-4796-5737","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078622553","display_name":"Zhihao Chang","orcid":"https://orcid.org/0000-0003-4627-7331"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihao Chang","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.45219046,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9878000020980835,"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":0.9878000020980835,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.004699999932199717,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.0008999999845400453,"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/discriminator","display_name":"Discriminator","score":0.730400025844574},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6208000183105469},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5181000232696533},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.49869999289512634},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.489300012588501},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4632999897003174},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.40290001034736633},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.3955000042915344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7486000061035156},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.730400025844574},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6208000183105469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5522000193595886},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5181000232696533},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.49869999289512634},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.489300012588501},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4632999897003174},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.40290001034736633},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3955000042915344},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38029998540878296},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.329800009727478},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3639448","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3639448","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/G1560732540","display_name":null,"funder_award_id":"62276205","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5498747354","display_name":null,"funder_award_id":"U22B2018","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6986719856","display_name":null,"funder_award_id":"62171357","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8628502939","display_name":null,"funder_award_id":"2024M762556","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W1965309615","https://openalex.org/W1971891445","https://openalex.org/W2603777577","https://openalex.org/W2786808285","https://openalex.org/W2794905319","https://openalex.org/W2803177391","https://openalex.org/W2904170036","https://openalex.org/W2910979321","https://openalex.org/W2962770389","https://openalex.org/W2987861506","https://openalex.org/W3035294798","https://openalex.org/W3096091382","https://openalex.org/W3133542152","https://openalex.org/W3175215201","https://openalex.org/W3183864931","https://openalex.org/W4289639938","https://openalex.org/W4304092618","https://openalex.org/W4312458427","https://openalex.org/W4313182973","https://openalex.org/W4313525856","https://openalex.org/W4321484009","https://openalex.org/W4385627184","https://openalex.org/W4387250073","https://openalex.org/W4387250813","https://openalex.org/W4390873603","https://openalex.org/W4391341572","https://openalex.org/W4400579912","https://openalex.org/W4400810737","https://openalex.org/W4401415787","https://openalex.org/W4401880010","https://openalex.org/W4402261644","https://openalex.org/W4403511252","https://openalex.org/W4404238668","https://openalex.org/W4404740834","https://openalex.org/W4405755231","https://openalex.org/W4405778600","https://openalex.org/W4415095228"],"related_works":[],"abstract_inverted_index":{"Cross-scene":[0],"hyperspectral":[1],"image":[2],"(HSI)":[3],"classification":[4,80],"faces":[5],"spectral-spatial":[6],"feature":[7,105],"distribution":[8,35],"shifts":[9],"resulting":[10],"from":[11],"cross-domain":[12,60],"heterogeneity,":[13],"which":[14],"has":[15],"become":[16],"a":[17,77,112,133,167],"critical":[18],"challenge":[19],"that":[20,66],"urgently":[21],"needs":[22],"resolution":[23],"in":[24,103,185],"the":[25,37,64,104,141,146,160,183,217],"field":[26],"of":[27,145,192],"remote":[28],"sensing":[29],"intelligent":[30],"interpretation.":[31],"To":[32,62,157],"address":[33],"this":[34,74,108,193],"shift,":[36],"mainstream":[38],"approach":[39],"is":[40,137,172,214],"Domain":[41],"Generalization":[42],"(DG).":[43],"However,":[44],"existing":[45,67],"HSI":[46,79],"DG":[47],"methods":[48,68],"primarily":[49],"focus":[50,70],"on":[51,71,115,195,216],"inter-class":[52,72],"separability,":[53,73],"while":[54],"paying":[55],"relatively":[56],"less":[57],"attention":[58],"to":[59,119,139,163,209],"transferability.":[61],"overcome":[63],"limitation":[65],"mainly":[69],"study":[75,109],"proposes":[76],"cross-scene":[78],"network,":[81],"termed":[82],"DPSKDnet.":[83],"By":[84],"synergistically":[85],"employing":[86],"dynamic":[87,116],"perturbation-based":[88],"destylization":[89,118],"and":[90,126,149,198,205],"self-knowledge":[91],"distillation":[92,170],"modeling":[93],"mechanisms,":[94],"DPSKDnet":[95],"constructs":[96],"domain-invariant":[97,165],"representations":[98,144],"with":[99],"strong":[100],"generalization":[101],"capabilities":[102],"space.":[106],"Specifically,":[107],"first":[110],"builds":[111],"generator":[113],"based":[114],"perturbation":[117],"mine":[120],"source":[121],"domain":[122,129,143],"(SD)":[123],"invariant":[124],"features":[125],"generate":[127],"extended":[128],"(ED)":[130],"samples.":[131],"Subsequently,":[132],"Fourier":[134],"Augmentation":[135],"Module":[136],"utilized":[138],"optimize":[140],"frequency":[142],"SD,":[147],"ED,":[148],"their":[150],"combination-generated":[151],"intermediate":[152],"domain,":[153],"obtaining":[154],"frequency-enhanced":[155],"representations.":[156],"effectively":[158],"improve":[159],"model\u2019s":[161],"ability":[162],"capture":[164],"features,":[166],"sample":[168],"pair":[169],"loss":[171],"devised.":[173],"This":[174],"loss,":[175],"informed":[176],"by":[177,202],"multi-domain":[178],"mixed":[179],"data":[180],"input,":[181],"guides":[182],"discriminator":[184],"online":[186],"self-supervised":[187],"learning.":[188],"The":[189,212],"overall":[190],"accuracy":[191],"method":[194],"Loukia,":[196],"Houston2018,":[197],"Pavia":[199],"Center":[200],"increased":[201],"0.48%,":[203],"0.81%,":[204],"1.5%,":[206],"respectively,":[207],"compared":[208],"state-of-the-art":[210],"methods.":[211],"code":[213],"available":[215],"website:":[218],"https://github.com/Yuhang-Hong/TGRS_DPSKDnet.":[219]},"counts_by_year":[],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-12-02T00:00:00"}