{"id":"https://openalex.org/W3007396525","doi":"https://doi.org/10.1109/bigdata47090.2019.9006120","title":"Remote Sensing Object Localization with Deep Heterogeneous Superpixel Features","display_name":"Remote Sensing Object Localization with Deep Heterogeneous Superpixel Features","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007396525","doi":"https://doi.org/10.1109/bigdata47090.2019.9006120","mag":"3007396525"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006120","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006120","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5108539576","display_name":"Alex Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alex Yang","raw_affiliation_strings":["Department of Department of Electrical & Computer Science, University of Missouri, Columbia, Missouri, USA"],"affiliations":[{"raw_affiliation_string":"Department of Department of Electrical & Computer Science, University of Missouri, Columbia, Missouri, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009311723","display_name":"J. Alex Hurt","orcid":"https://orcid.org/0000-0002-7234-1301"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J. Alex Hurt","raw_affiliation_strings":["Department of Department of Electrical & Computer Science, University of Missouri, Columbia, Missouri, USA"],"affiliations":[{"raw_affiliation_string":"Department of Department of Electrical & Computer Science, University of Missouri, Columbia, Missouri, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053850871","display_name":"Charlie Veal","orcid":null},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charlie T. Veal","raw_affiliation_strings":["Department of Department of Electrical & Computer Science, University of Missouri, Columbia, Missouri, USA"],"affiliations":[{"raw_affiliation_string":"Department of Department of Electrical & Computer Science, University of Missouri, Columbia, Missouri, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051712346","display_name":"Grant J. Scott","orcid":"https://orcid.org/0000-0001-5870-9387"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Grant J. Scott","raw_affiliation_strings":["Department of Department of Electrical & Computer Science, University of Missouri, Columbia, Missouri, USA"],"affiliations":[{"raw_affiliation_string":"Department of Department of Electrical & Computer Science, University of Missouri, Columbia, Missouri, USA","institution_ids":["https://openalex.org/I76835614"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5108539576"],"corresponding_institution_ids":["https://openalex.org/I76835614"],"apc_list":null,"apc_paid":null,"fwci":0.8927,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.79650257,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"10334","issue":null,"first_page":"5453","last_page":"5461"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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":0.9993000030517578,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8021484017372131},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7941809892654419},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6970314383506775},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6254633665084839},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.619076669216156},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5735533833503723},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5597185492515564},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5378415584564209},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5342456698417664},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5298445224761963},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.5281113386154175},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5268046855926514},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47655943036079407},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.43928924202919006},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.4131450653076172},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2538631558418274},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09107697010040283}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8021484017372131},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7941809892654419},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6970314383506775},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6254633665084839},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.619076669216156},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5735533833503723},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5597185492515564},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5378415584564209},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5342456698417664},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5298445224761963},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.5281113386154175},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5268046855926514},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47655943036079407},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.43928924202919006},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.4131450653076172},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2538631558418274},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09107697010040283},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006120","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006120","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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":50,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1839843765","https://openalex.org/W1861492603","https://openalex.org/W1980038761","https://openalex.org/W2031489346","https://openalex.org/W2102605133","https://openalex.org/W2118246710","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2588561483","https://openalex.org/W2592962403","https://openalex.org/W2613718673","https://openalex.org/W2618530766","https://openalex.org/W2695028939","https://openalex.org/W2725905437","https://openalex.org/W2763968347","https://openalex.org/W2785994986","https://openalex.org/W2788930825","https://openalex.org/W2796347433","https://openalex.org/W2804523038","https://openalex.org/W2808436940","https://openalex.org/W2811199523","https://openalex.org/W2893811450","https://openalex.org/W2900777837","https://openalex.org/W2948696683","https://openalex.org/W2962835968","https://openalex.org/W2963037989","https://openalex.org/W2963703618","https://openalex.org/W2964121744","https://openalex.org/W3099116041","https://openalex.org/W3103856189","https://openalex.org/W3106250896","https://openalex.org/W4248710273","https://openalex.org/W4293584584","https://openalex.org/W4297670610","https://openalex.org/W4297803092","https://openalex.org/W6620707391","https://openalex.org/W6628973269","https://openalex.org/W6637373629","https://openalex.org/W6638871335","https://openalex.org/W6639102338","https://openalex.org/W6687483927","https://openalex.org/W6743446608","https://openalex.org/W6748053814","https://openalex.org/W6748130322","https://openalex.org/W6750227808","https://openalex.org/W6764454148","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4327500857","https://openalex.org/W2106002764","https://openalex.org/W2965994363","https://openalex.org/W4311223090","https://openalex.org/W1689909837","https://openalex.org/W4205729548","https://openalex.org/W1579998189","https://openalex.org/W2172615727","https://openalex.org/W4298525700","https://openalex.org/W1895541646"],"abstract_inverted_index":{"Object":[0],"detection":[1,126],"and":[2,23,29,50,55,81,100,127],"localization":[3,84],"within":[4],"high-resolution":[5],"remote":[6],"sensing":[7],"imagery":[8],"(HR-RSI)":[9],"is":[10],"a":[11,15,93,144,151,159],"challenging":[12],"task":[13],"for":[14,46,124,134],"variety":[16],"of":[17,25,32,52,85,121,139,153,161,184],"reasons,":[18],"such":[19],"as":[20,129,131],"the":[21,26,30,33,38,47,107,119,136,154,180,185],"complexity":[22,51],"clutter":[24],"image":[27],"scene":[28,167],"compactness":[31],"intermixed":[34],"object":[35,75,83,125,168,190],"classes.":[36],"Even":[37],"most":[39],"comprehensive":[40],"training":[41],"datasets":[42],"cannot":[43],"adequately":[44],"account":[45],"rich":[48],"diversity":[49],"anthropogenic":[53],"objects":[54,140,182],"their":[56],"contextual":[57],"settings":[58],"in":[59,165],"large-scale":[60],"HR-RSI":[61],"collections.":[62],"Recent":[63],"approaches":[64,72],"using":[65,150],"deep":[66,86,101,172],"learning":[67],"techniques":[68,164],"include":[69],"bounding":[70,145],"box":[71],"(e.g.,":[73,79],"YOLO),":[74],"nomination":[76],"then":[77],"recognition":[78,169],"R-CNN),":[80],"post-detection":[82],"neural":[87,102],"network":[88],"detections.":[89],"Herein,":[90],"we":[91,117],"propose":[92],"novel":[94],"technique":[95],"that":[96,170],"leverages":[97],"heterogeneous":[98],"superpixels":[99],"feature":[103,173],"extraction":[104],"to":[105,143,175],"classify":[106],"superpixel":[108],"segmentation":[109],"through":[110],"relational":[111],"analysis.":[112],"In":[113],"this":[114,122],"preliminary":[115],"research,":[116],"demonstrate":[118],"validity":[120],"approach":[123],"localization,":[128],"well":[130],"its":[132],"suitability":[133],"identifying":[135],"irregular":[137],"shapes":[138],"(as":[141],"opposed":[142],"box).":[146],"Experiments":[147],"are":[148],"performed":[149],"sub-set":[152],"xView":[155],"benchmark":[156],"dataset":[157],"with":[158],"goal":[160],"spearheading":[162],"future":[163],"cluttered":[166],"allows":[171],"extractors":[174],"have":[176],"more":[177],"focus":[178],"on":[179],"target":[181],"instead":[183],"surrounding":[186],"area":[187],"or":[188],"nearby":[189],"pixels.":[191]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}