{"id":"https://openalex.org/W2894660140","doi":"https://doi.org/10.1109/tii.2018.2873492","title":"Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images","display_name":"Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2894660140","doi":"https://doi.org/10.1109/tii.2018.2873492","mag":"2894660140"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2018.2873492","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2018.2873492","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Industrial Informatics","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/A5065504878","display_name":"Ning Lv","orcid":"https://orcid.org/0000-0003-4091-714X"},"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":"Ning Lv","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111598130","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0002-4971-5029"},"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":"Chen Chen","raw_affiliation_strings":["State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-4971-5029","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037428599","display_name":"Tie Qiu","orcid":"https://orcid.org/0000-0003-2324-2523"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tie Qiu","raw_affiliation_strings":["School of Computer Science and Technology, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0003-2324-2523","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tianjin University, Tianjin, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072241102","display_name":"Arun Kumar Sangaiah","orcid":"https://orcid.org/0000-0002-0229-2460"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Arun Kumar Sangaiah","raw_affiliation_strings":["School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India"],"raw_orcid":"https://orcid.org/0000-0002-0229-2460","affiliations":[{"raw_affiliation_string":"School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":18.2335,"has_fulltext":false,"cited_by_count":216,"citation_normalized_percentile":{"value":0.99318063,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"14","issue":"12","first_page":"5530","last_page":"5538"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994999766349792,"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":0.9994999766349792,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.97079998254776,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7975181341171265},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.79232257604599},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6757903099060059},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6752024292945862},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6719830632209778},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6554378867149353},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6066654920578003},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.6059185862541199},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5163470506668091},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.499194860458374},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4922775328159332},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4810643196105957},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.43261343240737915},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42815446853637695},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3780791759490967},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.3250828981399536},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2704286575317383}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7975181341171265},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.79232257604599},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6757903099060059},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6752024292945862},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6719830632209778},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6554378867149353},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6066654920578003},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.6059185862541199},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5163470506668091},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.499194860458374},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4922775328159332},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4810643196105957},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.43261343240737915},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42815446853637695},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3780791759490967},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.3250828981399536},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2704286575317383},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2018.2873492","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2018.2873492","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G1376233665","display_name":null,"funder_award_id":"2016YFE0123000","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6411212857","display_name":null,"funder_award_id":"U1709218","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6538997798","display_name":"\u9762\u5411\u7269\u8054\u7f51\u4e2d\u5927\u89c4\u6a21\u5f02\u6784\u611f\u77e5\u7f51\u7edc\u9c81\u68d2\u6027\u6a21\u578b\u4e0e\u7b56\u7565\u7814\u7a76","funder_award_id":"61672131","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G654108453","display_name":null,"funder_award_id":"YS2017YFGH000872","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7843701004","display_name":null,"funder_award_id":"61571338","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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1836458662","https://openalex.org/W1964967908","https://openalex.org/W1965126626","https://openalex.org/W1998866072","https://openalex.org/W2031601791","https://openalex.org/W2049061912","https://openalex.org/W2056281790","https://openalex.org/W2076198115","https://openalex.org/W2086576628","https://openalex.org/W2090822373","https://openalex.org/W2091231747","https://openalex.org/W2096310027","https://openalex.org/W2110519070","https://openalex.org/W2153198964","https://openalex.org/W2221448138","https://openalex.org/W2295862745","https://openalex.org/W2516616494","https://openalex.org/W2521868507","https://openalex.org/W2574739416","https://openalex.org/W2615543373","https://openalex.org/W2627081599","https://openalex.org/W2628688992","https://openalex.org/W2745993090","https://openalex.org/W2752414286","https://openalex.org/W2773780472","https://openalex.org/W2788805965","https://openalex.org/W2791901417"],"related_works":["https://openalex.org/W2065648684","https://openalex.org/W2113052720","https://openalex.org/W2799624451","https://openalex.org/W2009383287","https://openalex.org/W2042914788","https://openalex.org/W2182190754","https://openalex.org/W4321264664","https://openalex.org/W2055824452","https://openalex.org/W2121688719","https://openalex.org/W2016481886"],"abstract_inverted_index":{"Image":[0],"segmentation":[1,22],"based":[2,153],"on":[3,154],"superpixel":[4,57,117],"is":[5,48,88,113,149],"used":[6,89],"in":[7,30,71,126,182],"urban":[8],"and":[9,158,178],"land":[10],"cover":[11],"change":[12,54,65,98,107,124,142,176,183,192],"detection":[13,184,193],"for":[14,101,185],"fast":[15],"locating":[16],"region":[17],"of":[18,104],"interest.":[19],"However,":[20],"the":[21,52,72,82,97,102,116,123,129,141,146],"algorithms":[23],"often":[24],"degrade":[25],"due":[26],"to":[27,50,90,96,121,139],"speckle":[28],"noise":[29,59,173],"synthetic":[31,186],"aperture":[32,187],"radar":[33,188],"images.":[34],"In":[35],"this":[36,133],"paper,":[37],"a":[38,43,110],"feature":[39,55],"learning":[40,168],"method":[41,148],"using":[42],"stacked":[44],"contractive":[45],"autoencoder":[46],"(sCAE)":[47],"presented":[49],"extract":[51],"temporal":[53,64,69],"from":[56,175],"with":[58,115,151],"suppression.":[60],"First,":[61],"an":[62],"affiliated":[63],"image,":[66],"which":[67,93],"obtains":[68],"difference":[70],"pixel":[73],"level,":[74],"are":[75,136],"built":[76],"by":[77,132],"three":[78],"different":[79],"metrics.":[80],"Second,":[81],"simple":[83],"linear":[84],"iterative":[85],"clustering":[86],"algorithm":[87],"generate":[91],"superpixels,":[92],"tightly":[94],"adhere":[95],"image":[99],"boundaries":[100],"purpose":[103],"acquiring":[105],"homogeneous":[106],"samples.":[108],"Third,":[109],"sCAE":[111,134],"network":[112],"trained":[114],"samples":[118],"as":[119],"input":[120],"learn":[122],"features":[125,131,177],"semantic.":[127],"Then,":[128],"encoded":[130],"model":[135,169],"binary":[137],"classified":[138],"create":[140],"result":[143],"map.":[144],"Finally,":[145],"proposed":[147],"compared":[150],"methods":[152],"principal":[155],"components":[156],"analysis":[157],"Markov":[159],"random":[160],"fields.":[161],"Experiment":[162],"results":[163],"show":[164],"that":[165],"our":[166],"deep":[167],"can":[170],"separate":[171],"nonlinear":[172],"efficiently":[174],"obtain":[179],"better":[180],"performance":[181],"images":[189],"than":[190],"conventional":[191],"algorithms.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":33},{"year":2022,"cited_by_count":36},{"year":2021,"cited_by_count":44},{"year":2020,"cited_by_count":38},{"year":2019,"cited_by_count":28},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}