{"id":"https://openalex.org/W4224298786","doi":"https://doi.org/10.3390/rs14091970","title":"SHAP-Based Interpretable Object Detection Method for Satellite Imagery","display_name":"SHAP-Based Interpretable Object Detection Method for Satellite Imagery","publication_year":2022,"publication_date":"2022-04-19","ids":{"openalex":"https://openalex.org/W4224298786","doi":"https://doi.org/10.3390/rs14091970"},"language":"en","primary_location":{"id":"doi:10.3390/rs14091970","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14091970","pdf_url":"https://www.mdpi.com/2072-4292/14/9/1970/pdf?version=1650435605","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/14/9/1970/pdf?version=1650435605","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065448886","display_name":"Hiroki Kawauchi","orcid":"https://orcid.org/0000-0002-3711-6053"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hiroki Kawauchi","raw_affiliation_strings":["Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081271255","display_name":"Takashi Fuse","orcid":"https://orcid.org/0000-0001-5789-252X"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takashi Fuse","raw_affiliation_strings":["Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5065448886"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":1.9358,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.87186068,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"14","issue":"9","first_page":"1970","last_page":"1970"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991999864578247,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9991999864578247,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.994700014591217,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9894000291824341,"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/computer-science","display_name":"Computer science","score":0.8105161190032959},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6979681253433228},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6920473575592041},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6261512041091919},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.4883677363395691},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47430017590522766},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4524768590927124},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4436548054218292},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4332331418991089},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4230911433696747},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.41563665866851807},{"id":"https://openalex.org/keywords/attribution","display_name":"Attribution","score":0.41539159417152405},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3996654152870178},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3885006606578827},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07149586081504822}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8105161190032959},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6979681253433228},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6920473575592041},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6261512041091919},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.4883677363395691},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47430017590522766},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4524768590927124},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4436548054218292},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4332331418991089},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4230911433696747},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.41563665866851807},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.41539159417152405},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3996654152870178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3885006606578827},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07149586081504822},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs14091970","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14091970","pdf_url":"https://www.mdpi.com/2072-4292/14/9/1970/pdf?version=1650435605","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:acbde93a7c264fdab7400ca2fdef7ad4","is_oa":true,"landing_page_url":"https://doaj.org/article/acbde93a7c264fdab7400ca2fdef7ad4","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 9, p 1970 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/9/1970/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14091970","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 14; Issue 9; Pages: 1970","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14091970","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14091970","pdf_url":"https://www.mdpi.com/2072-4292/14/9/1970/pdf?version=1650435605","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.800000011920929,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4224298786.pdf","grobid_xml":"https://content.openalex.org/works/W4224298786.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W177964384","https://openalex.org/W1787224781","https://openalex.org/W1849277567","https://openalex.org/W1861492603","https://openalex.org/W2008989859","https://openalex.org/W2026596024","https://openalex.org/W2031489346","https://openalex.org/W2100119660","https://openalex.org/W2120432001","https://openalex.org/W2163605009","https://openalex.org/W2195388612","https://openalex.org/W2282821441","https://openalex.org/W2605409611","https://openalex.org/W2618851150","https://openalex.org/W2744999500","https://openalex.org/W2785760873","https://openalex.org/W2891503716","https://openalex.org/W2901309398","https://openalex.org/W2917767525","https://openalex.org/W2919115771","https://openalex.org/W2951911250","https://openalex.org/W2963150697","https://openalex.org/W2963499661","https://openalex.org/W2963811535","https://openalex.org/W2988916019","https://openalex.org/W2992240579","https://openalex.org/W3037755158","https://openalex.org/W3110585990","https://openalex.org/W3186608063","https://openalex.org/W6607332302","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6734194636","https://openalex.org/W6743073161","https://openalex.org/W6759934792"],"related_works":["https://openalex.org/W2035546108","https://openalex.org/W2376361520","https://openalex.org/W2133328864","https://openalex.org/W2323536476","https://openalex.org/W2093949997","https://openalex.org/W2570200690","https://openalex.org/W2389726244","https://openalex.org/W3030478661","https://openalex.org/W2128730003","https://openalex.org/W4299904075"],"abstract_inverted_index":{"There":[0],"is":[1,42,65],"a":[2,22,85,105,121],"growing":[3],"need":[4],"for":[5,38,55,60,138,158,190],"algorithms":[6,16],"to":[7,67,74,101,123],"automatically":[8],"detect":[9],"objects":[10],"in":[11,25,34],"satellite":[12,114],"images.":[13],"Object":[14],"detection":[15,27,111],"using":[17,129,171],"deep":[18],"learning":[19],"have":[20,32],"demonstrated":[21],"significant":[23],"improvement":[24],"object":[26,110],"performance.":[28],"However,":[29],"deep-learning":[30,106],"models":[31,112],"difficulty":[33,41],"interpreting":[35],"the":[36,61,76,96,102,109,125,152,155,165,169,172,184,191],"features":[37,100],"inference.":[39],"This":[40],"practically":[43],"problematic":[44],"when":[45],"analyzing":[46],"earth-observation":[47],"images,":[48],"which":[49],"are":[50],"often":[51],"used":[52],"as":[53],"evidence":[54],"public":[56],"decision-making.":[57],"In":[58],"addition,":[59],"same":[62],"reason,":[63],"it":[64],"difficult":[66],"set":[68],"an":[69,91],"explicit":[70],"policy":[71],"or":[72],"criteria":[73],"improve":[75],"models.":[77],"To":[78],"deal":[79],"with":[80,116],"these":[81],"challenges,":[82],"we":[83,119,134],"introduce":[84],"feature":[86,131,147],"attribution":[87,97],"method":[88,122,175,188],"that":[89,168],"defines":[90],"approximate":[92],"model":[93,139,162,170],"and":[94,142,161,179,183],"calculates":[95],"of":[98,104,113,127,154,194],"input":[99],"output":[103],"model.":[107],"For":[108],"images":[115],"complex":[117],"textures,":[118],"propose":[120,135],"visualize":[124],"basis":[126,159],"inference":[128],"pixel-wise":[130],"attribution.":[132,148],"Furthermore,":[133],"new":[136,195],"methods":[137,157],"evaluation,":[140],"regularization,":[141],"data":[143,186],"selection,":[144],"based":[145],"on":[146],"Experimental":[149],"results":[150,166],"demonstrate":[151],"feasibility":[153],"proposed":[156,173,185],"visualization":[160],"evaluation.":[163],"Moreover,":[164],"illustrate":[167],"regularization":[174],"can":[176],"avoid":[177],"over-fitting":[178],"achieve":[180],"higher":[181],"performance,":[182],"selection":[187,193],"allows":[189],"efficient":[192],"training":[196],"data.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2022-04-26T00:00:00"}