{"id":"https://openalex.org/W2988638360","doi":"https://doi.org/10.1109/igarss.2019.8897938","title":"A Class Activation Mapping Guided Adversarial Training Method for Land-Use Classification and Object Detection","display_name":"A Class Activation Mapping Guided Adversarial Training Method for Land-Use Classification and Object Detection","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2988638360","doi":"https://doi.org/10.1109/igarss.2019.8897938","mag":"2988638360"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8897938","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8897938","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5026257114","display_name":"Rui Yang","orcid":"https://orcid.org/0000-0002-1783-9051"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Yang","raw_affiliation_strings":["School of Electronic Information, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058602237","display_name":"Xin Xu","orcid":"https://orcid.org/0000-0001-9211-6606"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Xu","raw_affiliation_strings":["School of Electronic Information, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041660006","display_name":"Zhaozhuo Xu","orcid":"https://orcid.org/0000-0001-9555-1830"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhaozhuo Xu","raw_affiliation_strings":["Electrical Engineering Department, Stanford University, Stanford, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Stanford University, Stanford, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069400785","display_name":"Chujiang Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chujiang Ding","raw_affiliation_strings":["School of Electronic Information, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081961287","display_name":"Fangling Pu","orcid":"https://orcid.org/0000-0002-1490-0347"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangling Pu","raw_affiliation_strings":["School of Electronic Information, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"9474","last_page":"9477"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9800000190734863,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9800000190734863,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9700999855995178,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9607999920845032,"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/adversarial-system","display_name":"Adversarial system","score":0.8548195362091064},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6983336806297302},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.667428195476532},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6589637994766235},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6446442008018494},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5991644859313965},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5285272598266602},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5250625014305115},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4732545018196106},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4697955846786499},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4488716721534729},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.348926305770874},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17546671628952026},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11208179593086243}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8548195362091064},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6983336806297302},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.667428195476532},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6589637994766235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6446442008018494},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5991644859313965},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5285272598266602},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5250625014305115},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4732545018196106},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4697955846786499},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4488716721534729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.348926305770874},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17546671628952026},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11208179593086243},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8897938","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8897938","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1945616565","https://openalex.org/W1980038761","https://openalex.org/W2295107390","https://openalex.org/W2512351403","https://openalex.org/W2515866431","https://openalex.org/W2607037079","https://openalex.org/W2765793020","https://openalex.org/W2774989306","https://openalex.org/W2796347433","https://openalex.org/W2962851944","https://openalex.org/W2962858109","https://openalex.org/W2963207607","https://openalex.org/W2963374347","https://openalex.org/W3105577662","https://openalex.org/W4293584584","https://openalex.org/W6637373629","https://openalex.org/W6639204139","https://openalex.org/W6640425456","https://openalex.org/W6685133223","https://openalex.org/W6750227808"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W4394050964","https://openalex.org/W3211393740","https://openalex.org/W3208049411","https://openalex.org/W3022908591","https://openalex.org/W4285706568","https://openalex.org/W2551249631"],"abstract_inverted_index":{"Interpretation":[0],"of":[1,10,50,62,70,149],"convolutional":[2],"neural":[3],"networks":[4],"(CNNs)":[5],"critically":[6],"influence":[7],"our":[8],"understanding":[9],"deep":[11],"learning":[12,113],"models\u2019":[13],"internal":[14],"dynamics.":[15],"In":[16],"this":[17,106],"paper,":[18],"we":[19,93],"demonstrate":[20],"an":[21],"interpretable":[22],"training":[23,31,54,88,122],"method,":[24],"namely":[25],"class":[26,47],"activation":[27,48,57],"mapping":[28],"guided":[29],"adversarial":[30],"(CAMAT),":[32],"for":[33,102,111],"two":[34],"typical":[35],"remote":[36,140],"sensing":[37,141],"tasks,":[38],"land-use":[39],"classification":[40],"and":[41,92,114,134,147],"object":[42],"detection.":[43],"We":[44],"first":[45],"generate":[46],"maps":[49],"the":[51,68,75,78,87,95,100,108,121,125,144,150],"current":[52],"batch":[53],"samples.":[55],"Class":[56],"map":[58,65],"is":[59],"a":[60,71],"kind":[61],"class-specific":[63],"saliency":[64],"that":[66],"quantifies":[67],"contributions":[69],"particular":[72],"region":[73],"in":[74,86,120,132],"image":[76],"to":[77],"CNN":[79],"prediction":[80],"result.":[81],"Then,":[82],"high":[83],"contribution":[84],"regions":[85],"samples":[89],"are":[90,117],"occluded,":[91],"leverage":[94],"partial":[96],"masked":[97],"images":[98],"as":[99],"inputs":[101],"network":[103,112],"training.":[104],"Following":[105],"paradigm,":[107],"key":[109],"areas":[110],"decision":[115],"making":[116],"purposefully":[118],"disturbed":[119],"phase,":[123],"thus":[124],"trained":[126],"model":[127],"could":[128],"have":[129],"better":[130],"performance":[131],"robustness":[133],"generalization.":[135],"Experiments":[136],"conducted":[137],"on":[138],"classic":[139],"datasets":[142],"verified":[143],"outperforming":[145],"effectiveness":[146],"efficiency":[148],"proposed":[151],"CAMAT.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}