{"id":"https://openalex.org/W4402261071","doi":"https://doi.org/10.1109/igarss53475.2024.10640400","title":"A Causality-Erasing Baseline Values of SHAP Method for Enhancing Explainable Deep Networks in SAR Target Classification","display_name":"A Causality-Erasing Baseline Values of SHAP Method for Enhancing Explainable Deep Networks in SAR Target Classification","publication_year":2024,"publication_date":"2024-07-07","ids":{"openalex":"https://openalex.org/W4402261071","doi":"https://doi.org/10.1109/igarss53475.2024.10640400"},"language":"en","primary_location":{"id":"doi:10.1109/igarss53475.2024.10640400","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss53475.2024.10640400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 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/A5101666280","display_name":"Zhiyuan Yang","orcid":"https://orcid.org/0009-0002-6008-566X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyuan Yang","raw_affiliation_strings":["University of Electronic Science and Technology of China,School of Information and Communication,Chengdu,P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,School of Information and Communication,Chengdu,P.R.China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061245960","display_name":"Zongyong Cui","orcid":"https://orcid.org/0000-0003-1155-786X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongyong Cui","raw_affiliation_strings":["University of Electronic Science and Technology of China,School of Information and Communication,Chengdu,P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,School of Information and Communication,Chengdu,P.R.China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101530504","display_name":"Zongjie Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongjie Cao","raw_affiliation_strings":["University of Electronic Science and Technology of China,School of Information and Communication,Chengdu,P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,School of Information and Communication,Chengdu,P.R.China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1996","last_page":"1999"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9986000061035156,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9986000061035156,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.994700014591217,"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":0.9817000031471252,"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/baseline","display_name":"Baseline (sea)","score":0.8309869170188904},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.644905686378479},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5274865031242371},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4805636703968048},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3229047656059265},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09812521934509277},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.052631378173828125}],"concepts":[{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.8309869170188904},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.644905686378479},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5274865031242371},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4805636703968048},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3229047656059265},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09812521934509277},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.052631378173828125},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss53475.2024.10640400","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss53475.2024.10640400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.4300000071525574}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2052493079","https://openalex.org/W2969880113","https://openalex.org/W3093764775","https://openalex.org/W3205196764","https://openalex.org/W4364388218"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2372385138","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"The":[0],"extensive":[1],"adoption":[2],"of":[3,19,43,50],"deep":[4],"learning":[5],"techniques":[6,31],"in":[7,16,53,102],"SAR":[8,54,71],"image":[9],"interpretation":[10],"has":[11],"spurred":[12],"a":[13,23,62],"heightened":[14],"interest":[15],"the":[17,48,57,84,89,103],"explainability":[18,25],"these":[20],"models.":[21],"SHAP,":[22],"masking-based":[24],"method,":[26],"relies":[27],"on":[28,56,67,83],"conventional":[29],"occlusion":[30],"(baseline":[32],"values)":[33],"that":[34,88],"are":[35],"frequently":[36],"overly":[37],"rigid":[38],"and":[39,70],"introduce":[40,61],"varying":[41],"degrees":[42],"interference.":[44],"To":[45],"faithfully":[46],"capture":[47],"influence":[49],"distinct":[51],"regions":[52],"images":[55],"overall":[58],"interpretation,":[59],"we":[60],"baseline":[63,90],"value":[64],"algorithm":[65,75,95],"based":[66],"multivariate":[68],"interactions":[69],"clutter":[72],"distribution.":[73],"This":[74],"is":[76],"applied":[77],"to":[78],"SHAP":[79],"attribution.":[80],"Experimental":[81],"results":[82],"MSTAR":[85],"dataset":[86],"illustrate":[87],"values":[91],"computed":[92],"by":[93],"our":[94],"effectively":[96],"eliminate":[97],"all":[98],"existing":[99],"significant":[100],"patterns":[101],"original":[104],"samples":[105],"without":[106],"introducing":[107],"new":[108],"patterns.":[109]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}