{"id":"https://openalex.org/W4416650000","doi":"https://doi.org/10.1109/tgrs.2025.3637094","title":"GA-MAE: Gradient-Guided Activation-Aware Masked Autoencoder for Remote Sensing Image Classification","display_name":"GA-MAE: Gradient-Guided Activation-Aware Masked Autoencoder for Remote Sensing Image Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416650000","doi":"https://doi.org/10.1109/tgrs.2025.3637094"},"language":null,"primary_location":{"id":"doi:10.1109/tgrs.2025.3637094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3637094","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":"https://openalex.org/A5100729473","display_name":"Xiangyu Xu","orcid":"https://orcid.org/0009-0008-5469-4972"},"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":"Xiangyu Xu","raw_affiliation_strings":["Xidian University, Xi&#x2019;an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University, Xi&#x2019;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&#x2019;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 Ministry of Education, School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-7372-9180","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi&#x2019;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&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054248958","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 Ministry of Education, School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi&#x2019;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&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024219043","display_name":"M. Li","orcid":null},"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":"Mengchang Li","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi&#x2019;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&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":null,"display_name":"Gechang Yao","orcid":null},"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":"Gechang Yao","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi&#x2019;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&#x2019;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":0.762,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79773386,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"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":0.9434000253677368,"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":0.9434000253677368,"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":0.02370000071823597,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.006300000008195639,"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/autoencoder","display_name":"Autoencoder","score":0.8130999803543091},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.6473000049591064},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5077000260353088},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5073999762535095},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.504800021648407},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.4731999933719635},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4415999948978424},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43130001425743103}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.820900022983551},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8130999803543091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6611999869346619},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.6473000049591064},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5077000260353088},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5073999762535095},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.504800021648407},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.4731999933719635},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4415999948978424},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43130001425743103},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36169999837875366},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.35120001435279846},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3637094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3637094","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/G6077883238","display_name":null,"funder_award_id":"2024M762556","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G6735595969","display_name":null,"funder_award_id":"XJSJ24069","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/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W2111809230","https://openalex.org/W2752782242","https://openalex.org/W2890133123","https://openalex.org/W2945177640","https://openalex.org/W2947295162","https://openalex.org/W2962858109","https://openalex.org/W2963091558","https://openalex.org/W2983376237","https://openalex.org/W3026303927","https://openalex.org/W3136987292","https://openalex.org/W3161622534","https://openalex.org/W3168316785","https://openalex.org/W3168367808","https://openalex.org/W4206522033","https://openalex.org/W4280490589","https://openalex.org/W4283697304","https://openalex.org/W4288391270","https://openalex.org/W4288391486","https://openalex.org/W4289752563","https://openalex.org/W4312592451","https://openalex.org/W4312804044","https://openalex.org/W4313156423","https://openalex.org/W4321483963","https://openalex.org/W4362500802","https://openalex.org/W4382469100","https://openalex.org/W4384130981","https://openalex.org/W4384161871","https://openalex.org/W4384915783","https://openalex.org/W4385348479","https://openalex.org/W4386221015","https://openalex.org/W4386590518","https://openalex.org/W4388543795","https://openalex.org/W4389778595","https://openalex.org/W4390480870","https://openalex.org/W4390691257","https://openalex.org/W4391168980","https://openalex.org/W4391594031","https://openalex.org/W4393153184","https://openalex.org/W4399074152","https://openalex.org/W4404708688","https://openalex.org/W4404788913","https://openalex.org/W4404809282","https://openalex.org/W4405361103"],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,8,20,31,43,79,90,99,124,129,150,153,164,177],"field":[2],"of":[3,22,45,92,94,132,152,171,186],"remote":[4],"sensing":[5],"self-supervised":[6,9,58],"learning,":[7],"learning":[10,21,59],"paradigm":[11],"based":[12],"on":[13,106,141,176],"masked":[14],"image":[15],"modeling":[16],"(MIM)":[17],"effectively":[18,41,56],"promotes":[19],"structural":[23],"and":[24,77,147,174,180,189],"contextual":[25],"semantic":[26,46,86],"information":[27,47,88,131],"in":[28,36,98],"images.":[29,49],"However,":[30],"random":[32],"masking":[33,104],"strategies":[34],"employed":[35],"past":[37],"studies":[38],"have":[39],"not":[40],"utilized":[42],"distribution":[44,87],"within":[48],"Furthermore,":[50],"overly":[51],"simple":[52],"reconstruction":[53,96,112],"tasks":[54],"cannot":[55],"improve":[57],"performance.":[60],"To":[61],"address":[62],"these":[63],"challenges,":[64],"this":[65,74],"paper":[66],"introduces":[67],"a":[68],"gradient-guided":[69],"activation-aware":[70],"autoencoder":[71],"(GA-MAE).":[72],"Specifically,":[73],"approach":[75],"computes":[76],"captures":[78],"spatial":[80],"spectral":[81],"activation":[82],"map":[83],"(SSAM)":[84],"with":[85,110],"during":[89],"process":[91],"backpropagation":[93],"spatial-spectral":[95],"loss":[97],"pre-training":[100],"phase,":[101],"thereby":[102],"enabling":[103],"operations":[105],"similar":[107],"visual":[108,133],"patches":[109,134],"higher":[111],"loss.":[113],"Additionally,":[114],"we":[115],"introduce":[116],"an":[117],"Activation-Aware":[118],"Self-Attention":[119],"mechanism":[120],"(ASA)":[121],"that":[122],"adjusts":[123],"self-attention":[125],"dependencies":[126],"by":[127,136],"utilizing":[128],"weight":[130],"provided":[135],"SSAM.":[137],"Extensive":[138],"experiments":[139],"conducted":[140],"three":[142],"public":[143],"datasets":[144],"(Houston2013,":[145],"Augsburg,":[146,179],"Berlin)":[148],"demonstrate":[149],"effectiveness":[151],"proposed":[154,165],"approach.":[155],"With":[156],"only":[157],"ten":[158],"labeled":[159],"training":[160],"samples":[161],"per":[162],"class,":[163],"method":[166],"achieves":[167],"anoverall":[168],"accuracy":[169],"(OA)":[170],"91.24%,":[172],"84.69%,":[173],"73.43%":[175],"Houston2013,":[178],"Berlin":[181],"datasets,":[182],"respectively,":[183],"indicating":[184],"improvements":[185],"1.09%,":[187],"2.05%,":[188],"2.40%":[190],"over":[191],"state-of-the-art":[192],"methods.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-12-11T00:21:10.989143","created_date":"2025-11-25T00:00:00"}
