{"id":"https://openalex.org/W3161329626","doi":"https://doi.org/10.1109/aipr50011.2020.9425056","title":"Methods of Exploiting Multispectral Imagery for the Monitoring of Illicit Coca Fields","display_name":"Methods of Exploiting Multispectral Imagery for the Monitoring of Illicit Coca Fields","publication_year":2020,"publication_date":"2020-10-13","ids":{"openalex":"https://openalex.org/W3161329626","doi":"https://doi.org/10.1109/aipr50011.2020.9425056","mag":"3161329626"},"language":"en","primary_location":{"id":"doi:10.1109/aipr50011.2020.9425056","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr50011.2020.9425056","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5086146678","display_name":"Emily E. Berkson","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":"Emily Berkson","raw_affiliation_strings":["Lockheed Martin ATC, Palo Alto, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin ATC, Palo Alto, CA","institution_ids":["https://openalex.org/I1287521167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033727646","display_name":"Austen Groener","orcid":"https://orcid.org/0000-0002-6508-2938"},"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, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin Space, King of Prussia, PA","institution_ids":["https://openalex.org/I1287521167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086918143","display_name":"Charlene Cuellar-Vite","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":"Charlene Cuellar-Vite","raw_affiliation_strings":["Lockheed Martin ATC, Palo Alto, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin ATC, Palo Alto, CA","institution_ids":["https://openalex.org/I1287521167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020852742","display_name":"Gary Chern","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":"Gary Chern","raw_affiliation_strings":["Lockheed Martin ATC, Palo Alto, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin ATC, Palo Alto, CA","institution_ids":["https://openalex.org/I1287521167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023711809","display_name":"Stephen O\u2019Neill","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":"Stephen O'Neill","raw_affiliation_strings":["Lockheed Martin Space, King of Prussia, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin Space, King of Prussia, PA","institution_ids":["https://openalex.org/I1287521167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083061362","display_name":"Michael J. 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, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin Space, King of Prussia, PA","institution_ids":["https://openalex.org/I1287521167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065923867","display_name":"Tyler Bartelmo","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":"Tyler Bartelmo","raw_affiliation_strings":["Lockheed Martin Space, King of Prussia, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin Space, King of Prussia, PA","institution_ids":["https://openalex.org/I1287521167"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002558495","display_name":"Mark D. 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, King of Prussia, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lockheed Martin Space, King of Prussia, PA","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40184887,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"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":0.9983000159263611,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.926800012588501,"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/multispectral-image","display_name":"Multispectral image","score":0.8786298036575317},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.8074204921722412},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7537813186645508},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7486228346824646},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6374597549438477},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6357948184013367},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.565498411655426},{"id":"https://openalex.org/keywords/coca","display_name":"Coca","score":0.4905194640159607},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.46296975016593933},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4427063763141632},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.43140313029289246},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42882055044174194},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3974308371543884},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3050310015678406},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.24764007329940796},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0662553608417511}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.8786298036575317},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.8074204921722412},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7537813186645508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7486228346824646},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6374597549438477},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6357948184013367},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.565498411655426},{"id":"https://openalex.org/C2781313679","wikidata":"https://www.wikidata.org/wiki/Q66793593","display_name":"Coca","level":2,"score":0.4905194640159607},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.46296975016593933},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4427063763141632},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.43140313029289246},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42882055044174194},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3974308371543884},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3050310015678406},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.24764007329940796},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0662553608417511},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aipr50011.2020.9425056","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr50011.2020.9425056","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1582058549","https://openalex.org/W1617570115","https://openalex.org/W1901129140","https://openalex.org/W2010319424","https://openalex.org/W2087347434","https://openalex.org/W2101234009","https://openalex.org/W2108598243","https://openalex.org/W2114486983","https://openalex.org/W2162480849","https://openalex.org/W2295820431","https://openalex.org/W2560023338","https://openalex.org/W2565639579","https://openalex.org/W2599765304","https://openalex.org/W2606306511","https://openalex.org/W2735039185","https://openalex.org/W2911692648","https://openalex.org/W2942265585","https://openalex.org/W2950141105","https://openalex.org/W2953106684","https://openalex.org/W2981630388","https://openalex.org/W2999607073","https://openalex.org/W3081444111","https://openalex.org/W3100828714","https://openalex.org/W3101577715","https://openalex.org/W3105636206","https://openalex.org/W3132455321","https://openalex.org/W4214564766","https://openalex.org/W4233760599","https://openalex.org/W4288325606","https://openalex.org/W4300420063","https://openalex.org/W4388375774","https://openalex.org/W6620707391","https://openalex.org/W6634825034","https://openalex.org/W6675354045","https://openalex.org/W6735463952","https://openalex.org/W6761981288","https://openalex.org/W6764322716","https://openalex.org/W6772750526"],"related_works":["https://openalex.org/W2738862710","https://openalex.org/W3176438653","https://openalex.org/W2981628807","https://openalex.org/W4379875147","https://openalex.org/W3012393889","https://openalex.org/W3189091156","https://openalex.org/W3014041368","https://openalex.org/W4386087993","https://openalex.org/W4285815841","https://openalex.org/W3193641238"],"abstract_inverted_index":{"State-of-the-art":[0],"deep":[1],"learning":[2,14],"(DL)":[3],"algorithms":[4,47,110,144],"for":[5],"automatic":[6],"target":[7],"recognition":[8],"(ATR)":[9],"typically":[10],"rely":[11],"on":[12,18,113,153],"transfer":[13],"from":[15,185],"networks":[16,172],"pre-trained":[17],"RGB":[19,115,135,154,187],"imagery.":[20,139,158],"In":[21,57],"this":[22,60],"work,":[23],"we":[24,160],"investigate":[25],"the":[26,39,62,123,133],"benefits":[27],"of":[28,42,45,59,69,164,177],"using":[29,91],"additional":[30],"spectral":[31],"information":[32],"contained":[33],"in":[34,55,72],"multispectral":[35,67,175],"satellite":[36],"imagery,":[37,74,120],"with":[38,76,102,148],"targeted":[40],"development":[41],"a":[43,65,149,162],"suite":[44],"ATR":[46],"to":[48,98,167,173],"detect":[49],"and":[50,94,105,117,155],"segment":[51],"illicit":[52],"coca":[53,70],"fields":[54,71],"Colombia.":[56],"support":[58],"effort,":[61],"team":[63],"curated":[64],"custom":[66],"dataset":[68],"WorldView-3":[73],"starting":[75],"Sentinel-2":[77],"annotations":[78],"provided":[79],"by":[80],"expert":[81],"image":[82,125],"analysts.":[83],"We":[84],"present":[85],"baseline":[86],"land":[87],"cover":[88],"classification":[89],"results":[90,97],"traditional":[92],"methods,":[93],"compare":[95],"these":[96],"detection":[99],"metrics":[100,130],"obtained":[101],"Faster":[103],"R-CNN":[104],"Mask":[106],"R-CNN.":[107],"The":[108],"DL":[109],"are":[111,145],"run":[112],"classic":[114],"imagery":[116,136],"false":[118,156],"color":[119,157],"which":[121],"exploits":[122],"near-infrared":[124],"content.":[126],"Mean":[127],"average":[128],"precision":[129],"indicate":[131],"that":[132],"standard":[134],"outperforms":[137],"false-color":[138],"Deep":[140],"learning-based":[141],"semantic":[142],"segmentation":[143],"also":[146],"investigated,":[147],"comparison":[150],"between":[151],"training":[152],"Finally,":[159],"include":[161],"discussion":[163],"ongoing":[165],"research":[166],"modify":[168],"state-of-the-art":[169],"convolutional":[170],"neural":[171],"accept":[174],"inputs":[176],"more":[178],"than":[179],"three":[180],"channels,":[181],"while":[182],"still":[183],"benefitting":[184],"pretrained":[186],"weights.":[188]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
