{"id":"https://openalex.org/W2010733777","doi":"https://doi.org/10.1109/igarss.2014.6945950","title":"An mean shift algorithm with adaptive bandwidth and weight selection for high spatial remotely sensed imagery segmentation","display_name":"An mean shift algorithm with adaptive bandwidth and weight selection for high spatial remotely sensed imagery segmentation","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W2010733777","doi":"https://doi.org/10.1109/igarss.2014.6945950","mag":"2010733777"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2014.6945950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2014.6945950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Geoscience and Remote Sensing Symposium","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/A5114137440","display_name":"Qinling Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]},{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinling Dai","raw_affiliation_strings":["School of Forestry, Southwest Forestry University, Kunming, China","School of Printing and Packaging, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Forestry, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]},{"raw_affiliation_string":"School of Printing and Packaging, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108980607","display_name":"Leiguang Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]},{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CA","CN"],"is_corresponding":false,"raw_author_name":"Leiguang Wang","raw_affiliation_strings":["Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, Canada","School of Forestry, Southwest Forestry University, Kunming, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, Canada","institution_ids":["https://openalex.org/I106938459"]},{"raw_affiliation_string":"School of Forestry, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103190126","display_name":"Qizhi Xu","orcid":"https://orcid.org/0000-0002-0136-4418"},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Qizhi Xu","raw_affiliation_strings":["Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, Canada","institution_ids":["https://openalex.org/I106938459"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100356830","display_name":"Yun Zhang","orcid":"https://orcid.org/0000-0002-9464-1751"},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yun Zhang","raw_affiliation_strings":["Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, Canada","institution_ids":["https://openalex.org/I106938459"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13098631,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1592","last_page":"1595"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"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":0.9932000041007996,"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/segmentation","display_name":"Segmentation","score":0.721531867980957},{"id":"https://openalex.org/keywords/mean-shift","display_name":"Mean-shift","score":0.6886982917785645},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6765633225440979},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6614153385162354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6558424234390259},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.639459490776062},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6366223096847534},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5741229057312012},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5688401460647583},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5379970073699951},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.49991297721862793},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48055794835090637},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4616056978702545},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.44768595695495605},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32565122842788696},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26969265937805176},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10291692614555359}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.721531867980957},{"id":"https://openalex.org/C48548287","wikidata":"https://www.wikidata.org/wiki/Q6803557","display_name":"Mean-shift","level":3,"score":0.6886982917785645},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6765633225440979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6614153385162354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6558424234390259},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.639459490776062},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6366223096847534},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5741229057312012},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5688401460647583},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5379970073699951},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.49991297721862793},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48055794835090637},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4616056978702545},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.44768595695495605},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32565122842788696},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26969265937805176},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10291692614555359},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2014.6945950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2014.6945950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1984792953","https://openalex.org/W2033367204","https://openalex.org/W2067191022","https://openalex.org/W2100435576","https://openalex.org/W2114468928","https://openalex.org/W2119823327","https://openalex.org/W2122069218","https://openalex.org/W2153635508","https://openalex.org/W2171033594","https://openalex.org/W2243481691","https://openalex.org/W2981849677","https://openalex.org/W6690777518","https://openalex.org/W6769377118"],"related_works":["https://openalex.org/W2023748438","https://openalex.org/W2124385053","https://openalex.org/W2169903804","https://openalex.org/W2352790313","https://openalex.org/W2355406465","https://openalex.org/W1024431332","https://openalex.org/W2545971808","https://openalex.org/W2142709933","https://openalex.org/W2376628591","https://openalex.org/W1994653991"],"abstract_inverted_index":{"An":[0],"improved":[1],"mean":[2,105],"shift":[3,106],"segmentation":[4],"method":[5,93],"featuring":[6],"adaptive":[7],"parameter":[8],"selection":[9],"is":[10,51],"presented":[11],"in":[12,24,32],"this":[13],"paper.":[14],"We":[15,58],"associate":[16],"the":[17,70,79,91,96,104],"bandwidths":[18],"and":[19,38,65,108],"weight":[20,37],"for":[21,40],"each":[22,41],"point":[23],"a":[25,47,74],"spatial-range":[26],"feature":[27],"space":[28],"with":[29,103],"boundary":[30,48,85],"information":[31,98],"an":[33],"image":[34],"plane.":[35],"Varying":[36],"bandwidth":[39],"pixel":[42],"are":[43],"assigned":[44],"according":[45],"to":[46],"map,":[49],"which":[50],"obtained":[52],"by":[53],"learning":[54,76],"multiple":[55],"edge":[56,63],"cues.":[57],"consider":[59],"two":[60,66],"groups":[61],"of":[62,111],"cues":[64],"regressing":[67],"modules,":[68],"approach":[69],"cue":[71],"combination":[72],"as":[73],"supervised":[75],"problem":[77],"from":[78,100],"ground":[80],"truth":[81],"data":[82],"(manually":[83],"sketched":[84],"maps).":[86],"From":[87],"our":[88],"preliminary":[89],"results,":[90],"provided":[92],"can":[94],"combine":[95],"top-down":[97],"got":[99],"regression":[101],"models":[102],"process":[107],"constrain":[109],"over-clustering":[110],"pixels":[112],"belonging":[113],"different":[114],"land":[115],"objects.":[116]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}