{"id":"https://openalex.org/W3081482411","doi":"https://doi.org/10.1109/aipr47015.2019.9174598","title":"Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features","display_name":"Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3081482411","doi":"https://doi.org/10.1109/aipr47015.2019.9174598","mag":"3081482411"},"language":"en","primary_location":{"id":"doi:10.1109/aipr47015.2019.9174598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr47015.2019.9174598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2009.04866","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Michael Harner","orcid":null},"institutions":[{"id":"https://openalex.org/I1287521167","display_name":"Lockheed Martin (United States)","ror":"https://ror.org/026er9r08","country_code":"US","type":"company","lineage":["https://openalex.org/I1287521167"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Harner","raw_affiliation_strings":["Lockheed Martin Space, King of Prussia, Pennsylvania King of Prussia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin Space, King of Prussia, Pennsylvania King of Prussia","institution_ids":["https://openalex.org/I1287521167"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Austen Groener","orcid":null},"institutions":[{"id":"https://openalex.org/I1287521167","display_name":"Lockheed Martin (United States)","ror":"https://ror.org/026er9r08","country_code":"US","type":"company","lineage":["https://openalex.org/I1287521167"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Austen Groener","raw_affiliation_strings":["Lockheed Martin Space, King of Prussia, Pennsylvania King of Prussia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin Space, King of Prussia, Pennsylvania King of Prussia","institution_ids":["https://openalex.org/I1287521167"]}]},{"author_position":"last","author":{"id":null,"display_name":"Mark Pritt","orcid":null},"institutions":[{"id":"https://openalex.org/I1287521167","display_name":"Lockheed Martin (United States)","ror":"https://ror.org/026er9r08","country_code":"US","type":"company","lineage":["https://openalex.org/I1287521167"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark Pritt","raw_affiliation_strings":["Lockheed Martin Space, Gaithersburg, Maryland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin Space, Gaithersburg, Maryland","institution_ids":["https://openalex.org/I1287521167"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1287521167"],"apc_list":null,"apc_paid":null,"fwci":1.7512,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.88104535,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9976999759674072,"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"}},"topics":[{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9976999759674072,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9944999814033508,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9760000109672546,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.490200012922287},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4871000051498413},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46709999442100525},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.44859999418258667},{"id":"https://openalex.org/keywords/local-binary-patterns","display_name":"Local binary patterns","score":0.44760000705718994},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4465999901294708},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.37470000982284546},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.36000001430511475}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6794999837875366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6195999979972839},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5321999788284302},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.490200012922287},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4871000051498413},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4853000044822693},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46709999442100525},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.44859999418258667},{"id":"https://openalex.org/C87335442","wikidata":"https://www.wikidata.org/wiki/Q2494345","display_name":"Local binary patterns","level":4,"score":0.44760000705718994},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4465999901294708},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.37470000982284546},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.36000001430511475},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C2985301230","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite image","level":3,"score":0.3140999972820282},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2685999870300293},{"id":"https://openalex.org/C205049153","wikidata":"https://www.wikidata.org/wiki/Q2698605","display_name":"Polarization (electrochemistry)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.25459998846054077}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/aipr47015.2019.9174598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr47015.2019.9174598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2009.04866","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.04866","pdf_url":"https://arxiv.org/pdf/2009.04866","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2009.04866","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.04866","pdf_url":"https://arxiv.org/pdf/2009.04866","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2044465660","https://openalex.org/W2100293215","https://openalex.org/W2102021091","https://openalex.org/W2113545279","https://openalex.org/W2900773919","https://openalex.org/W2979776509","https://openalex.org/W4388480101"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3,69],"present":[4],"a":[5,32,54,71,76,81,127],"methodology":[6,41],"for":[7,85,138],"monitoring":[8,134],"man-made,":[9],"construction-like":[10],"activities":[11],"in":[12,38,92,111],"low-resolution":[13],"SAR":[14,63],"imagery.":[15,64],"Our":[16],"source":[17],"of":[18,50,53,61,120],"data":[19],"is":[20,131],"the":[21,59,93,100,135],"European":[22],"Space":[23],"Agency's":[24],"Sentinel-l":[25],"satellite":[26],"which":[27],"provides":[28],"global":[29],"coverage":[30],"at":[31],"12-day":[33],"revisit":[34],"rate.":[35],"Despite":[36],"limitations":[37],"resolution,":[39],"our":[40,104],"enables":[42],"us":[43],"to":[44,103,114],"monitor":[45],"activity":[46,122],"levels":[47],"(i.e.":[48],"presence":[49],"vehicles,":[51],"equipment)":[52],"pre-defined":[55],"location":[56],"by":[57],"analyzing":[58],"texture":[60,90],"detected":[62],"Using":[65],"an":[66],"exploratory":[67],"dataset,":[68],"trained":[70],"support":[72],"vector":[73],"machine":[74],"(SVM),":[75],"random":[77],"binary":[78],"forest,":[79],"and":[80,95,140],"fully-connected":[82],"neural":[83],"network":[84],"classification.":[86],"We":[87],"use":[88],"Haralick":[89],"features":[91,102],"VV":[94],"VH":[96],"polarization":[97],"channels":[98],"as":[99],"input":[101],"classifiers.":[105],"Each":[106],"classifier":[107],"showed":[108],"promising":[109],"results":[110],"being":[112],"able":[113],"distinguish":[115],"between":[116],"two":[117],"possible":[118],"types":[119],"construction-site":[121],"levels.":[123],"This":[124],"paper":[125],"documents":[126],"case":[128],"study":[129],"that":[130],"centered":[132],"around":[133],"construction":[136],"process":[137],"oil":[139],"gas":[141],"fracking":[142],"wells.":[143]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2020-09-01T00:00:00"}
