{"id":"https://openalex.org/W3077140934","doi":"https://doi.org/10.1109/lgrs.2020.3012140","title":"Band Selection With the Explanatory Gradient Saliency Maps of Convolutional Neural Networks","display_name":"Band Selection With the Explanatory Gradient Saliency Maps of Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-08-17","ids":{"openalex":"https://openalex.org/W3077140934","doi":"https://doi.org/10.1109/lgrs.2020.3012140","mag":"3077140934"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.3012140","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3012140","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5101663006","display_name":"Lei Zhao","orcid":"https://orcid.org/0000-0002-9320-0188"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lei Zhao","raw_affiliation_strings":["Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing, China","School of Information Science and Technology, Beijing Forestry University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing, China","institution_ids":["https://openalex.org/I4210134523"]},{"raw_affiliation_string":"School of Information Science and Technology, Beijing Forestry University, Beijing, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100458986","display_name":"Yi Zeng","orcid":"https://orcid.org/0009-0001-5784-5659"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Zeng","raw_affiliation_strings":["Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing, China","School of Information Science and Technology, Beijing Forestry University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing, China","institution_ids":["https://openalex.org/I4210134523"]},{"raw_affiliation_string":"School of Information Science and Technology, Beijing Forestry University, Beijing, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023925857","display_name":"Peng Liu","orcid":"https://orcid.org/0000-0003-3292-8551"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Liu","raw_affiliation_strings":["Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058955724","display_name":"Xiaohui Su","orcid":"https://orcid.org/0000-0001-7608-0797"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohui Su","raw_affiliation_strings":["Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing, China","School of Information Science and Technology, Beijing Forestry University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing, China","institution_ids":["https://openalex.org/I4210134523"]},{"raw_affiliation_string":"School of Information Science and Technology, Beijing Forestry University, Beijing, China","institution_ids":["https://openalex.org/I31683504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101663006"],"corresponding_institution_ids":["https://openalex.org/I31683504","https://openalex.org/I4210134523"],"apc_list":null,"apc_paid":null,"fwci":1.8865,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.89134988,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"17","issue":"12","first_page":"2105","last_page":"2109"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.996999979019165,"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":0.996999979019165,"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/T10320","display_name":"Neural Networks and Applications","score":0.9940999746322632,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9925000071525574,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8203394412994385},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7990900874137878},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6727349758148193},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6493658423423767},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5628955960273743},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5037402510643005},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5034682154655457},{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.4819352626800537},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.44459208846092224}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8203394412994385},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7990900874137878},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6727349758148193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6493658423423767},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5628955960273743},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5037402510643005},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5034682154655457},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.4819352626800537},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.44459208846092224},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2020.3012140","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3012140","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G435834338","display_name":null,"funder_award_id":"31872240","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5538849239","display_name":null,"funder_award_id":"61731022","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6834434948","display_name":null,"funder_award_id":"41971397","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1932531222","https://openalex.org/W2071821878","https://openalex.org/W2138038253","https://openalex.org/W2154053567","https://openalex.org/W2292865806","https://openalex.org/W2294445779","https://openalex.org/W2340318445","https://openalex.org/W2500751094","https://openalex.org/W2518815253","https://openalex.org/W2518897583","https://openalex.org/W2769143033","https://openalex.org/W2896340099","https://openalex.org/W2900127593","https://openalex.org/W2937638900","https://openalex.org/W2962851944","https://openalex.org/W3105005050","https://openalex.org/W6685133223"],"related_works":["https://openalex.org/W2888673113","https://openalex.org/W2056065966","https://openalex.org/W2352602608","https://openalex.org/W4293226380","https://openalex.org/W2062641654","https://openalex.org/W2212288070","https://openalex.org/W3149975758","https://openalex.org/W1517786189","https://openalex.org/W4386564352","https://openalex.org/W2952668426"],"abstract_inverted_index":{"In":[0,31],"most":[1],"of":[2,21,44,67,70,74,85],"the":[3,7,11,28,42,65,68,82,86,89,96,106,111,134,142],"existing":[4],"studies":[5],"on":[6,120],"band":[8,36],"selection":[9,37],"using":[10,110],"convolutional":[12],"neural":[13],"networks":[14],"(CNNs),":[15],"there":[16],"is":[17,39,47,56,77,116],"no":[18],"exact":[19],"explanation":[20],"how":[22],"feature":[23,45],"learning":[24],"helps":[25],"to":[26,60,81,94],"find":[27],"important":[29],"bands.":[30],"this":[32],"letter,":[33],"a":[34,52,75],"CNN-based":[35],"method":[38],"presented,":[40],"and":[41,58,100,124],"process":[43],"tracing":[46],"explained":[48],"in":[49],"detail.":[50],"First,":[51],"1-D":[53],"CNN":[54],"model":[55,115,136],"designed":[57],"trained":[59],"reach":[61],"high":[62],"accuracy.":[63],"Next,":[64],"derivative":[66,90],"sum":[69],"partial":[71],"output":[72],"combinations":[73],"layer":[76,84],"obtained":[78],"with":[79,126],"respect":[80],"input":[83],"CNN.":[87],"Then,":[88],"maps":[91,99,103],"are":[92,108],"used":[93],"obtain":[95],"sample":[97],"saliency":[98,102],"class":[101],"(CSMs).":[104],"Finally,":[105],"bands":[107],"selected":[109],"CSMs.":[112],"The":[113,130],"proposed":[114,135],"verified":[117],"by":[118],"experiments":[119],"different":[121],"data":[122],"sets":[123],"compared":[125],"other":[127,143],"related":[128],"methods.":[129,144],"results":[131],"show":[132],"that":[133],"can":[137],"achieve":[138],"better":[139],"performance":[140],"than":[141]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}