{"id":"https://openalex.org/W7123342703","doi":"https://doi.org/10.1109/access.2026.3652311","title":"Dual-Stream Masked Autoencoder-Based Method for Hyperspectral and SAR Image Classification","display_name":"Dual-Stream Masked Autoencoder-Based Method for Hyperspectral and SAR Image Classification","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7123342703","doi":"https://doi.org/10.1109/access.2026.3652311"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3652311","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3652311","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3652311","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jian Wang","orcid":"https://orcid.org/0000-0001-9729-6052"},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Wang","raw_affiliation_strings":["School of Electronic Information, Xi&#x2019;an Polytechnic University, Lintong Campus, Xi&#x2019;an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0001-9729-6052","affiliations":[{"raw_affiliation_string":"School of Electronic Information, Xi&#x2019;an Polytechnic University, Lintong Campus, Xi&#x2019;an, Shaanxi, China","institution_ids":["https://openalex.org/I27599042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122863905","display_name":"Yuze Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuze Wen","raw_affiliation_strings":["School of Electronic Information, Xi&#x2019;an Polytechnic University, Lintong Campus, Xi&#x2019;an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0009-7940-2398","affiliations":[{"raw_affiliation_string":"School of Electronic Information, Xi&#x2019;an Polytechnic University, Lintong Campus, Xi&#x2019;an, Shaanxi, China","institution_ids":["https://openalex.org/I27599042"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122869248","display_name":"Xun Li","orcid":null},"institutions":[{"id":"https://openalex.org/I27599042","display_name":"Xi'an Polytechnic University","ror":"https://ror.org/03442p831","country_code":"CN","type":"education","lineage":["https://openalex.org/I27599042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xun Li","raw_affiliation_strings":["School of Electronic Information, Xi&#x2019;an Polytechnic University, Lintong Campus, Xi&#x2019;an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0002-3403-7990","affiliations":[{"raw_affiliation_string":"School of Electronic Information, Xi&#x2019;an Polytechnic University, Lintong Campus, Xi&#x2019;an, Shaanxi, China","institution_ids":["https://openalex.org/I27599042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27599042"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04058564,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"7528","last_page":"7542"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9337000250816345,"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":0.9337000250816345,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.014499999582767487,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.010300000198185444,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7210999727249146},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6898000240325928},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6270999908447266},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5468999743461609},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5432000160217285},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5296000242233276},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.477400004863739},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.42820000648498535},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.42800000309944153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.758899986743927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7386999726295471},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7210999727249146},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6898000240325928},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6270999908447266},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5468999743461609},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5432000160217285},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5296000242233276},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.477400004863739},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4758000075817108},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.42820000648498535},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.42489999532699585},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4196999967098236},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3995000123977661},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.361299991607666},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.329800009727478},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.296999990940094},{"id":"https://openalex.org/C114700698","wikidata":"https://www.wikidata.org/wiki/Q2882278","display_name":"Spectral bands","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.2856999933719635},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.26030001044273376},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.2572999894618988},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25270000100135803}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3652311","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3652311","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7ef2d995a1a24d7ea1bc6a666d533711","is_oa":true,"landing_page_url":"https://doaj.org/article/7ef2d995a1a24d7ea1bc6a666d533711","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 7528-7542 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3652311","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3652311","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4490615427494049,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G3658308886","display_name":null,"funder_award_id":"2025JC-YBQN-917","funder_id":"https://openalex.org/F4320326696","funder_display_name":"Jiangxi Provincial Department of Science and Technology"},{"id":"https://openalex.org/G5947801595","display_name":null,"funder_award_id":"23JK0462","funder_id":"https://openalex.org/F4320326696","funder_display_name":"Jiangxi Provincial Department of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320326696","display_name":"Jiangxi Provincial Department of Science and Technology","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2303172903","https://openalex.org/W2412588858","https://openalex.org/W2500751094","https://openalex.org/W2601564443","https://openalex.org/W2806155925","https://openalex.org/W3003326148","https://openalex.org/W4206025940","https://openalex.org/W4206578920","https://openalex.org/W4226196877","https://openalex.org/W4285296445","https://openalex.org/W4312593485","https://openalex.org/W4312698276","https://openalex.org/W4312723186","https://openalex.org/W4313156423","https://openalex.org/W4313646123","https://openalex.org/W4319069095","https://openalex.org/W4319865968","https://openalex.org/W4327522253","https://openalex.org/W4327662994","https://openalex.org/W4360770903","https://openalex.org/W4366678167","https://openalex.org/W4385245566","https://openalex.org/W4388543795","https://openalex.org/W4389104749","https://openalex.org/W4389104894","https://openalex.org/W4389161200"],"related_works":[],"abstract_inverted_index":{"Land-cover":[0],"classification":[1],"from":[2,142],"multi-source":[3,88,205],"remote":[4,206],"sensing":[5,207],"imagery":[6],"requires":[7],"the":[8,46,188],"effective":[9],"integration":[10],"of":[11,192],"complementary":[12,200],"spatial":[13,102,111,178],"and":[14,57,87,104,112,122,139,150,167,183,190,194],"spectral":[15,105,113],"information":[16,141],"across":[17],"heterogeneous":[18],"modalities,":[19],"which":[20],"is":[21],"crucial":[22],"for":[23,82,174,203],"accurately":[24],"capturing":[25,54,199],"complex":[26,177],"landscape":[27],"features":[28],"that":[29,155],"cannot":[30],"be":[31],"fully":[32],"represented":[33],"by":[34,135],"a":[35,72,94],"single":[36],"modality.":[37],"However,":[38],"conventional":[39],"masked":[40,101],"image":[41,89,208],"modeling":[42],"frameworks":[43],"such":[44,180],"as":[45,181],"Masked":[47,74],"Autoencoder":[48,75],"(MAE)":[49],"exhibit":[50],"limited":[51],"capability":[52],"in":[53,162,198],"cross-modal":[55,133],"dependencies":[56],"often":[58],"fail":[59],"to":[60,98,117],"preserve":[61],"fine-grained":[62],"structural":[63],"details.":[64],"To":[65],"overcome":[66],"these":[67],"limitations,":[68],"this":[69],"study":[70],"proposes":[71],"Dual-Stream":[73],"(DS-MAE),":[76],"an":[77],"attention-driven":[78],"pretraining\u2013training":[79],"framework":[80],"designed":[81],"joint":[83],"spatial\u2013spectral":[84,201],"representation":[85],"learning":[86],"classification.":[90,209],"The":[91],"model":[92],"incorporates":[93],"dual-stream":[95],"deformable-attention":[96],"Transformer":[97],"separately":[99],"reconstruct":[100],"pixels":[103],"channels,":[106],"while":[107],"Swin-Transformer":[108],"branches":[109],"along":[110],"directions":[114],"enhance":[115],"sensitivity":[116],"small":[118],"objects,":[119],"high-frequency":[120],"textures,":[121],"object":[123],"boundaries.":[124],"A":[125],"cascaded":[126],"feature":[127],"fusion":[128],"module":[129],"(CFM)":[130],"further":[131],"strengthens":[132],"alignment":[134],"effectively":[136],"integrating":[137],"global":[138],"local":[140],"different":[143],"modalities.":[144],"Experiments":[145],"conducted":[146],"on":[147],"Berlin,":[148],"Augsburg,":[149],"Houston":[151],"2018":[152],"datasets":[153],"demonstrate":[154],"DS-MAE":[156,193],"consistently":[157],"surpasses":[158],"seven":[159],"state-of-the-art":[160],"methods":[161],"Overall":[163],"Accuracy,":[164,166],"Average":[165],"Kappa":[168],"coefficient,":[169],"with":[170],"particularly":[171],"notable":[172],"gains":[173],"classes":[175],"exhibiting":[176],"structures":[179],"buildings":[182],"roads.":[184],"These":[185],"results":[186],"confirm":[187],"robustness":[189],"generalizability":[191],"highlight":[195],"its":[196],"effectiveness":[197],"cues":[202],"advanced":[204]},"counts_by_year":[],"updated_date":"2026-01-25T23:04:38.658462","created_date":"2026-01-14T00:00:00"}
