{"id":"https://openalex.org/W2601459726","doi":"https://doi.org/10.1109/iccnc.2017.7876261","title":"Change detection by deep neural networks for synthetic aperture radar images","display_name":"Change detection by deep neural networks for synthetic aperture radar images","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2601459726","doi":"https://doi.org/10.1109/iccnc.2017.7876261","mag":"2601459726"},"language":"en","primary_location":{"id":"doi:10.1109/iccnc.2017.7876261","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccnc.2017.7876261","pdf_url":null,"source":{"id":"https://openalex.org/S4306498181","display_name":"2017 International Conference on Computing, Networking and Communications (ICNC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Computing, Networking and Communications (ICNC)","raw_type":"proceedings-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/A5086588258","display_name":"Liao Frank","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Frank Liao","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, US"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, US","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025994184","display_name":"Koshelev Elizabeth","orcid":null},"institutions":[{"id":"https://openalex.org/I6902469","display_name":"Brandeis University","ror":"https://ror.org/05abbep66","country_code":"US","type":"education","lineage":["https://openalex.org/I6902469"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elizabeth Koshelev","raw_affiliation_strings":["Carnegie Mellon University, Brandeis University, Waltham, MA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Brandeis University, Waltham, MA","institution_ids":["https://openalex.org/I6902469","https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038393389","display_name":"Milton Malcolm","orcid":null},"institutions":[{"id":"https://openalex.org/I6902469","display_name":"Brandeis University","ror":"https://ror.org/05abbep66","country_code":"US","type":"education","lineage":["https://openalex.org/I6902469"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Malcolm Milton","raw_affiliation_strings":["Carnegie Mellon University, Brandeis University, Waltham, MA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Brandeis University, Waltham, MA","institution_ids":["https://openalex.org/I6902469","https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033732065","display_name":"Yuanwei Jin","orcid":"https://orcid.org/0000-0001-6764-8651"},"institutions":[{"id":"https://openalex.org/I9364636","display_name":"Salisbury University","ror":"https://ror.org/029gwvs11","country_code":"US","type":"education","lineage":["https://openalex.org/I9364636"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuanwei Jin","raw_affiliation_strings":["Salisbury University, Salisbury, MD"],"affiliations":[{"raw_affiliation_string":"Salisbury University, Salisbury, MD","institution_ids":["https://openalex.org/I9364636"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061179713","display_name":"Enyue Lu","orcid":"https://orcid.org/0009-0008-7283-4617"},"institutions":[{"id":"https://openalex.org/I22407884","display_name":"University of Maryland Eastern Shore","ror":"https://ror.org/006cymg18","country_code":"US","type":"education","lineage":["https://openalex.org/I22407884"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Enyue Lu","raw_affiliation_strings":["Princess Anne, University of Maryland Eastern Shore"],"affiliations":[{"raw_affiliation_string":"Princess Anne, University of Maryland Eastern Shore","institution_ids":["https://openalex.org/I22407884"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5086588258"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.3952,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.70213997,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"947","last_page":"951"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9972000122070312,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9972000122070312,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9926000237464905,"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/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.8637847304344177},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.8140110969543457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.76862633228302},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7426085472106934},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.666373610496521},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.648128867149353},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.6413869857788086},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.6249955892562866},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.5901337265968323},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5249174237251282},{"id":"https://openalex.org/keywords/inverse-synthetic-aperture-radar","display_name":"Inverse synthetic aperture radar","score":0.5150801539421082},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5019705295562744},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.47948867082595825},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4186590909957886},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.3822610080242157},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3223213851451874},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2963385581970215},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.076303631067276},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.068083256483078}],"concepts":[{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.8637847304344177},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.8140110969543457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.76862633228302},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7426085472106934},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.666373610496521},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.648128867149353},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.6413869857788086},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.6249955892562866},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.5901337265968323},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5249174237251282},{"id":"https://openalex.org/C109094680","wikidata":"https://www.wikidata.org/wiki/Q6060432","display_name":"Inverse synthetic aperture radar","level":4,"score":0.5150801539421082},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5019705295562744},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.47948867082595825},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4186590909957886},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.3822610080242157},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3223213851451874},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2963385581970215},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.076303631067276},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.068083256483078},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccnc.2017.7876261","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccnc.2017.7876261","pdf_url":null,"source":{"id":"https://openalex.org/S4306498181","display_name":"2017 International Conference on Computing, Networking and Communications (ICNC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Computing, Networking and Communications (ICNC)","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":10,"referenced_works":["https://openalex.org/W1964069486","https://openalex.org/W1964155876","https://openalex.org/W1990368529","https://openalex.org/W1997709480","https://openalex.org/W2027091505","https://openalex.org/W2113076747","https://openalex.org/W2116064496","https://openalex.org/W2133003941","https://openalex.org/W2165108236","https://openalex.org/W2221448138"],"related_works":["https://openalex.org/W2133290590","https://openalex.org/W2042914788","https://openalex.org/W2182190754","https://openalex.org/W4321264664","https://openalex.org/W2009383287","https://openalex.org/W2121688719","https://openalex.org/W2016481886","https://openalex.org/W2055824452","https://openalex.org/W2727313114","https://openalex.org/W1989852278"],"abstract_inverted_index":{"In":[0],"this":[1],"Research":[2],"Experience":[3],"for":[4,16,59,133],"Undergraduate":[5],"(REU)":[6],"project,":[7],"we":[8],"develop":[9],"and":[10,49,76,79,98,107,157],"implement":[11],"deep":[12,37,111],"neural":[13,26,41,112],"network":[14,42],"algorithms":[15],"change":[17,56,116,134],"detection":[18,57,117,135],"of":[19,51,136],"synthetic":[20,159],"aperture":[21,160],"radar":[22,161],"(SAR)":[23],"images.":[24,87,162],"Deep":[25],"networks":[27,113],"represent":[28],"a":[29,115,130],"powerful":[30],"data":[31],"processing":[32],"methodology":[33],"that":[34,103],"integrates":[35],"recent":[36],"learning":[38],"techniques":[39],"on":[40,150],"computing":[43],"frameworks":[44],"to":[45],"undercover":[46],"underlying":[47],"features":[48],"structures":[50],"observational":[52],"data.":[53],"The":[54,109],"classic":[55],"method":[58,94],"SAR":[60,121,138],"images":[61,122,139,152],"is":[62,143],"through":[63],"the":[64,71,82,85,91],"difference":[65,92,126],"image":[66,78,93],"analysis":[67],"method,":[68],"i.e.,":[69],"filtering":[70],"noise":[72,142,156],"in":[73],"each":[74],"before-change":[75],"after-change":[77],"then":[80],"identifying":[81],"changes":[83],"between":[84],"two":[86],"Although":[88],"well":[89],"researched,":[90],"requires":[95],"significant":[96],"pre-processing":[97],"has":[99],"difficulty":[100],"with":[101,153],"applications":[102],"require":[104],"high":[105],"accuracy":[106],"flexibility.":[108],"proposed":[110],"create":[114],"map":[118],"from":[119],"original":[120],"directly":[123],"without":[124],"generating":[125],"images,":[127],"thus":[128],"providing":[129],"novel":[131],"framework":[132],"complicated":[137],"where":[140],"speckle":[141,155],"also":[144],"present.":[145],"We":[146],"conduct":[147],"numerous":[148],"experiments":[149],"artificial":[151],"added":[154],"real-world":[158]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
