{"id":"https://openalex.org/W1974912395","doi":"https://doi.org/10.1109/igarss.2013.6723193","title":"Segmentation of very high resolution imagery using spectral and structural information","display_name":"Segmentation of very high resolution imagery using spectral and structural information","publication_year":2013,"publication_date":"2013-07-01","ids":{"openalex":"https://openalex.org/W1974912395","doi":"https://doi.org/10.1109/igarss.2013.6723193","mag":"1974912395"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2013.6723193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2013.6723193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS","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/A5100374914","display_name":"Jing Liu","orcid":"https://orcid.org/0000-0001-5207-7614"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jing Liu","raw_affiliation_strings":["Institute of Remote Sensing and GIS, Peking University, Beijing, China","Inst. of remote sensing & GIS, Peking Univ., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and GIS, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Inst. of remote sensing & GIS, Peking Univ., Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100742536","display_name":"Peijun Li","orcid":"https://orcid.org/0000-0002-4989-9892"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peijun Li","raw_affiliation_strings":["Institute of Remote Sensing and GIS, Peking University, Beijing, China","Inst. of remote sensing & GIS, Peking Univ., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and GIS, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Inst. of remote sensing & GIS, Peking Univ., Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100374914"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.0999374,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"12","issue":null,"first_page":"1967","last_page":"1970"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9941999912261963,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9904999732971191,"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/computer-science","display_name":"Computer science","score":0.7369916439056396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7351973652839661},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6730611324310303},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.6673367619514465},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6300157904624939},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.616157054901123},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5440752506256104},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5088394284248352},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4901910424232483},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4729093015193939},{"id":"https://openalex.org/keywords/region-growing","display_name":"Region growing","score":0.4392152428627014},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4127243161201477},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3723389208316803}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7369916439056396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7351973652839661},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6730611324310303},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.6673367619514465},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6300157904624939},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.616157054901123},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5440752506256104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5088394284248352},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4901910424232483},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4729093015193939},{"id":"https://openalex.org/C206824153","wikidata":"https://www.wikidata.org/wiki/Q1169834","display_name":"Region growing","level":5,"score":0.4392152428627014},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4127243161201477},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3723389208316803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2013.6723193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2013.6723193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS","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":8,"referenced_works":["https://openalex.org/W2003128786","https://openalex.org/W2114819256","https://openalex.org/W2115451191","https://openalex.org/W2148878936","https://openalex.org/W2160754664","https://openalex.org/W2163994077","https://openalex.org/W2981849677","https://openalex.org/W6769377118"],"related_works":["https://openalex.org/W2394279717","https://openalex.org/W2364730859","https://openalex.org/W3144569342","https://openalex.org/W2386644571","https://openalex.org/W2368273968","https://openalex.org/W2551987074","https://openalex.org/W4281943322","https://openalex.org/W4313052709","https://openalex.org/W2185902295","https://openalex.org/W2055202857"],"abstract_inverted_index":{"Image":[0],"Segmentation":[1],"is":[2,41,64,76,124],"an":[3],"essential":[4],"step":[5,75],"for":[6,29],"object-based":[7],"analysis":[8],"using":[9,57],"very":[10],"high":[11],"resolution":[12],"(VHR)":[13],"images.":[14,32],"A":[15,53,72],"segmentation":[16,70],"method":[17,34,113,121],"by":[18],"combined":[19,45],"spectral":[20,47],"and":[21,44,87,122],"structural":[22],"information":[23,40,48],"was":[24],"proposed":[25,98,112],"in":[26],"this":[27],"paper":[28],"VHR":[30],"multispectral":[31],"The":[33,100],"consists":[35],"of":[36,96,103],"three":[37],"steps.":[38],"Structural":[39],"first":[42],"extracted":[43],"with":[46,126],"to":[49,60,67,79,92],"define":[50],"pixel":[51],"similarity.":[52],"region":[54,73],"growing":[55],"strategy":[56],"gradient":[58],"image":[59,108],"determine":[61],"seed":[62],"points":[63],"then":[65],"employed":[66],"produce":[68],"initial":[69,82],"results.":[71,83],"merging":[74],"finally":[77],"performed":[78],"improve":[80],"the":[81,94,97,111,127],"Both":[84],"visual":[85],"inspection":[86],"quantitative":[88],"measures":[89],"are":[90],"used":[91,129],"evaluate":[93],"performance":[95,117],"method.":[99,132],"experimental":[101],"results":[102],"a":[104,115],"Beijing":[105],"area":[106],"Quickbird":[107],"indicate":[109],"that":[110],"achieves":[114],"better":[116],"than":[118],"existing":[119],"morphological":[120],"it":[123],"comparable":[125],"widely":[128],"eCognition":[130],"multi-resolution":[131]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
