{"id":"https://openalex.org/W2101250649","doi":"https://doi.org/10.1109/igarss.2008.4779149","title":"Texture Analysis and its Application for Single-Band SAR Thematic Information Extraction","display_name":"Texture Analysis and its Application for Single-Band SAR Thematic Information Extraction","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W2101250649","doi":"https://doi.org/10.1109/igarss.2008.4779149","mag":"2101250649"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2008.4779149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2008.4779149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2008 - 2008 IEEE International 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/A5069602887","display_name":"Deyong Hu","orcid":"https://orcid.org/0000-0001-7136-677X"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hu De-yong","raw_affiliation_strings":["College of Resource Environment & Tourism, Capital Normal University, Beijing, China","Key Lab. of 3D Inf. Acquisition & Applic., Minist. of Educ"],"affiliations":[{"raw_affiliation_string":"College of Resource Environment & Tourism, Capital Normal University, Beijing, China","institution_ids":["https://openalex.org/I96852419"]},{"raw_affiliation_string":"Key Lab. of 3D Inf. Acquisition & Applic., Minist. of Educ","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100461149","display_name":"Xiaojuan Li","orcid":"https://orcid.org/0000-0002-9217-597X"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Xiao-juan","raw_affiliation_strings":["College of Resource Environment & Tourism, Capital Normal University, Beijing, China","Key Lab. of 3D Inf. Acquisition & Applic., Minist. of Educ"],"affiliations":[{"raw_affiliation_string":"College of Resource Environment & Tourism, Capital Normal University, Beijing, China","institution_ids":["https://openalex.org/I96852419"]},{"raw_affiliation_string":"Key Lab. of 3D Inf. Acquisition & Applic., Minist. of Educ","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016721345","display_name":"Zhao Wen-ji","orcid":"https://orcid.org/0000-0003-4435-7143"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Wen-ji","raw_affiliation_strings":["College of Resource Environment & Tourism, Capital Normal University, Beijing, China","Key Lab. of 3D Inf. Acquisition & Applic., Minist. of Educ"],"affiliations":[{"raw_affiliation_string":"College of Resource Environment & Tourism, Capital Normal University, Beijing, China","institution_ids":["https://openalex.org/I96852419"]},{"raw_affiliation_string":"Key Lab. of 3D Inf. Acquisition & Applic., Minist. of Educ","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108059798","display_name":"Huili Gong","orcid":"https://orcid.org/0000-0001-8691-0264"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gong Hui-li","raw_affiliation_strings":["College of Resource Environment & Tourism, Capital Normal University, Beijing, China","Key Lab. of 3D Inf. Acquisition & Applic., Minist. of Educ"],"affiliations":[{"raw_affiliation_string":"College of Resource Environment & Tourism, Capital Normal University, Beijing, China","institution_ids":["https://openalex.org/I96852419"]},{"raw_affiliation_string":"Key Lab. of 3D Inf. Acquisition & Applic., Minist. of Educ","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5069602887"],"corresponding_institution_ids":["https://openalex.org/I96852419"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.11458787,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"II","last_page":"935"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.983299970626831,"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"}},"topics":[{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.983299970626831,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9611999988555908,"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":0.9223999977111816,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7040506601333618},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6547048091888428},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6498664021492004},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6051222085952759},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5833176374435425},{"id":"https://openalex.org/keywords/thematic-map","display_name":"Thematic map","score":0.5694395303726196},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5462347269058228},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.5172134637832642},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5168917179107666},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.4941242039203644},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4374263882637024},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40637463331222534},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3232252895832062},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32119566202163696},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2228735387325287},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1576768159866333},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.151419997215271},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.08875823020935059}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7040506601333618},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6547048091888428},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6498664021492004},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6051222085952759},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5833176374435425},{"id":"https://openalex.org/C93692415","wikidata":"https://www.wikidata.org/wiki/Q1502030","display_name":"Thematic map","level":2,"score":0.5694395303726196},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5462347269058228},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.5172134637832642},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5168917179107666},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.4941242039203644},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4374263882637024},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40637463331222534},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3232252895832062},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32119566202163696},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2228735387325287},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1576768159866333},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.151419997215271},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.08875823020935059},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2008.4779149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2008.4779149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2008 - 2008 IEEE International 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":11,"referenced_works":["https://openalex.org/W2039604733","https://openalex.org/W2041847302","https://openalex.org/W2042979134","https://openalex.org/W2073074687","https://openalex.org/W2080079057","https://openalex.org/W2109337265","https://openalex.org/W2112256737","https://openalex.org/W2127227873","https://openalex.org/W2151862721","https://openalex.org/W2159055592","https://openalex.org/W4252423951"],"related_works":["https://openalex.org/W2291311298","https://openalex.org/W2378021067","https://openalex.org/W2347703439","https://openalex.org/W2371027582","https://openalex.org/W2076654158","https://openalex.org/W4210966920","https://openalex.org/W2156233651","https://openalex.org/W2550009779","https://openalex.org/W2134401318","https://openalex.org/W1555506570"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"single-band":[3],"and":[4,46,54,71,80,82],"single-polarization":[5],"Radarsat-1":[6],"SAR":[7,91],"image":[8,13,56,92,99],"is":[9],"used":[10],"to":[11,28,108],"evaluate":[12],"classification":[14,100],"with":[15,77,95],"textural":[16,34,96],"analysis.":[17],"Firstly,":[18],"the":[19,30,55,69,83,98],"statistic":[20],"information":[21],"of":[22],"sample":[23],"were":[24,58],"analyzed":[25],"using":[26,60],"semivariogram":[27],"determine":[29],"optimum":[31],"parameters":[32],"for":[33,90,112],"extraction;":[35],"Then":[36],"four":[37],"textures":[38],"such":[39,116],"as":[40,117],"Homogeneity,":[41],"Mean,":[42],"Angle":[43],"Second":[44],"Moment":[45],"Entropy":[47],"had":[48],"been":[49],"calculated":[50],"based":[51,101],"on":[52,102],"GLCM,":[53],"data":[57],"processed":[59],"Support":[61],"Vector":[62],"Machine":[63],"classification.":[64],"The":[65],"results":[66],"show":[67],"that":[68],"water":[70],"settlement":[72,118],"areas":[73],"are":[74],"extracted":[75],"accurately":[76],"accuracy":[78,121],"99.34%":[79],"82.54%,":[81],"SVM":[84,103],"method":[85],"has":[86,104],"better":[87],"extension":[88],"ability":[89],"classification;":[93],"Assisting":[94],"information,":[97],"a":[105],"obvious":[106],"enhancement":[107],"original":[109],"SAR,":[110],"especially":[111],"some":[113],"complex":[114],"objects":[115],"areas(about":[119],"increasing":[120],"18%).":[122]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}