{"id":"https://openalex.org/W1956144543","doi":"https://doi.org/10.1109/igarss.2002.1027187","title":"Automatic thresholding abundance fractional images for mixed pixel classification","display_name":"Automatic thresholding abundance fractional images for mixed pixel classification","publication_year":2003,"publication_date":"2003-06-25","ids":{"openalex":"https://openalex.org/W1956144543","doi":"https://doi.org/10.1109/igarss.2002.1027187","mag":"1956144543"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2002.1027187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2002.1027187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE International Geoscience and Remote Sensing Symposium","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/A5108453370","display_name":"Shao-Shan Chiang","orcid":null},"institutions":[{"id":"https://openalex.org/I50519452","display_name":"Lunghwa University of Science and Technology","ror":"https://ror.org/001y2wd07","country_code":"TW","type":"education","lineage":["https://openalex.org/I50519452"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shao-Shan Chiang","raw_affiliation_strings":["Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I50519452"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050624952","display_name":"Chein\u2010I Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chein-I Chang","raw_affiliation_strings":["Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":null,"first_page":"3375","last_page":"3377"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9997000098228455,"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":0.9997000098228455,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9840999841690063,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9782000184059143,"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/thresholding","display_name":"Thresholding","score":0.8584521412849426},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.8349812626838684},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7525595426559448},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.7198483347892761},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6590171456336975},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5738601088523865},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5049145817756653},{"id":"https://openalex.org/keywords/binary-image","display_name":"Binary image","score":0.4767552316188812},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4211128354072571},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4194956123828888},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39597463607788086},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.30672842264175415}],"concepts":[{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.8584521412849426},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.8349812626838684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7525595426559448},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.7198483347892761},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6590171456336975},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5738601088523865},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5049145817756653},{"id":"https://openalex.org/C193828747","wikidata":"https://www.wikidata.org/wiki/Q864118","display_name":"Binary image","level":4,"score":0.4767552316188812},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4211128354072571},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4194956123828888},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39597463607788086},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.30672842264175415}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2002.1027187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2002.1027187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2117741752","https://openalex.org/W3020300317","https://openalex.org/W6776999251"],"related_works":["https://openalex.org/W2122667464","https://openalex.org/W2054831422","https://openalex.org/W2106731176","https://openalex.org/W2387104004","https://openalex.org/W3047671631","https://openalex.org/W1548186045","https://openalex.org/W2902098370","https://openalex.org/W2147223569","https://openalex.org/W4283267580","https://openalex.org/W3186605777"],"abstract_inverted_index":{"Mixed":[0],"pixel":[1,37],"classification":[2,8,27,38],"is":[3,39,68,79],"different":[4,61],"from":[5,21],"spatial-based":[6],"image":[7,97,101],"in":[9],"the":[10,13,31,47],"sense":[11],"that":[12],"former":[14],"deals":[15],"with":[16,60],"abundance":[17,49,89,95],"fractional":[18,50,90,96],"images":[19],"resulting":[20],"mixed":[22,36],"pixels":[23],"as":[24,107],"opposed":[25],"to":[26,70,87],"maps":[28],"produced":[29],"by":[30,43],"latter.":[32],"As":[33],"a":[34,76,99,103,108],"result,":[35],"generally":[40],"carried":[41],"out":[42],"visual":[44],"inspection":[45],"on":[46],"generated":[48],"images.":[51,91],"Consequently,":[52],"it":[53,67],"can":[54],"be":[55],"very":[56],"subjective":[57],"and":[58,74],"vary":[59],"human":[62],"interpretations.":[63],"Under":[64],"such":[65],"circumstance,":[66],"difficult":[69],"substantiate":[71],"an":[72,94],"algorithm":[73],"conducting":[75],"comparative":[77],"analysis":[78],"impossible.":[80],"This":[81],"paper":[82],"presents":[83],"one":[84],"histogram-based":[85],"approach":[86],"thresholding":[88],"It":[92],"thresholds":[93],"into":[98],"binary":[100],"using":[102],"probability":[104],"of":[105],"confidence":[106],"threshold":[109],"value.":[110]},"counts_by_year":[{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
