{"id":"https://openalex.org/W2150035316","doi":"https://doi.org/10.1109/igarss.2004.1369129","title":"A wavelet multiresolution technique for polarimetric texture analysis and segmentation of SAR images","display_name":"A wavelet multiresolution technique for polarimetric texture analysis and segmentation of SAR images","publication_year":2004,"publication_date":"2004-12-23","ids":{"openalex":"https://openalex.org/W2150035316","doi":"https://doi.org/10.1109/igarss.2004.1369129","mag":"2150035316"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2004.1369129","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2004.1369129","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004","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/A5098018514","display_name":"Dirk Hoekman Gianfranco De Grandi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dirk Hoekman Gianfranco De_Grandi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5098018514"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":29.2954,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.99142389,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"710","last_page":"713"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9919999837875366,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6981228590011597},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.6614264845848083},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6505115032196045},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.605436384677887},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.563057005405426},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5506547689437866},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5487639307975769},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5143483877182007},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5015912055969238},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45210617780685425},{"id":"https://openalex.org/keywords/polarimetry","display_name":"Polarimetry","score":0.4407816529273987},{"id":"https://openalex.org/keywords/multiresolution-analysis","display_name":"Multiresolution analysis","score":0.4211444556713104},{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.3083752989768982},{"id":"https://openalex.org/keywords/scattering","display_name":"Scattering","score":0.10705384612083435},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.07518655061721802},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07355824112892151}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6981228590011597},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6614264845848083},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6505115032196045},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.605436384677887},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.563057005405426},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5506547689437866},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5487639307975769},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5143483877182007},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5015912055969238},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45210617780685425},{"id":"https://openalex.org/C28493345","wikidata":"https://www.wikidata.org/wiki/Q899381","display_name":"Polarimetry","level":3,"score":0.4407816529273987},{"id":"https://openalex.org/C121927907","wikidata":"https://www.wikidata.org/wiki/Q1952516","display_name":"Multiresolution analysis","level":5,"score":0.4211444556713104},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.3083752989768982},{"id":"https://openalex.org/C191486275","wikidata":"https://www.wikidata.org/wiki/Q210028","display_name":"Scattering","level":2,"score":0.10705384612083435},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.07518655061721802},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07355824112892151}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2004.1369129","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2004.1369129","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1984295062","https://openalex.org/W2004217976","https://openalex.org/W2008826820","https://openalex.org/W2100860054","https://openalex.org/W2114059901","https://openalex.org/W2149520480","https://openalex.org/W4214540058"],"related_works":["https://openalex.org/W2026860918","https://openalex.org/W2035593284","https://openalex.org/W2225281849","https://openalex.org/W2612145225","https://openalex.org/W2059660610","https://openalex.org/W2469797871","https://openalex.org/W2546962038","https://openalex.org/W1511691694","https://openalex.org/W2139293182","https://openalex.org/W2013198648"],"abstract_inverted_index":{"A":[0,114],"technique":[1,133,170],"is":[2,31,48,69,97,101,124,142,190],"presented":[3],"for":[4],"multiscale":[5,20],"texture":[6,137,175],"analysis":[7],"and":[8,41,53,84],"segmentation":[9,138,176],"of":[10,33,37,42,64,75,87,93,107,116,131,147,165,168,177],"polarimetric":[11,58,90],"SAR":[12,173],"images.":[13],"Textural":[14],"features":[15],"are":[16,159],"extracted":[17],"using":[18,54],"a":[19,25,62,73,82,148,178],"wavelet":[21,26,44],"decomposition":[22,47],"based":[23,71,119],"on":[24,72,120],"frame.":[27],"The":[28,46],"feature":[29],"vector":[30],"composed":[32],"local":[34],"variance":[35],"estimates":[36],"the":[38,43,76,88,108,129,132,145,166,169,184],"smooth":[39],"image":[40,181],"coefficients.":[45],"performed":[49],"at":[50,61,188],"two":[51,152],"scales":[52],"images":[55],"derived":[56],"by":[57,103,183],"power":[59],"synthesis":[60],"set":[63,68,115,125],"polarization":[65,79],"configurations.":[66],"This":[67],"chosen":[70],"priori-knowledge":[74],"texturally":[77],"optimal":[78],"states.":[80],"Alternatively":[81],"complete":[83],"nonredundant":[85],"representation":[86],"full":[89],"information":[91],"consisting":[92],"nine":[94],"backscatter":[95],"intensities":[96],"used.":[98],"Feature":[99],"reduction":[100],"achieved":[102],"an":[104,163],"approximate":[105],"solution":[106],"Multiple":[109],"Discriminant":[110],"Analysis":[111],"(MDA)":[112],"transform.":[113],"controlled":[117],"experiments,":[118],"Monte":[121],"Carlo":[122],"simulations,":[123],"up":[126],"to":[127,136,171],"assess":[128],"performance":[130],"with":[134,155],"respect":[135],"problems.":[139],"One":[140],"case":[141],"reported":[143],"concerning":[144],"simulation":[146],"fragmented":[149],"forest,":[150],"where":[151],"vegetation":[153],"classes":[154],"different":[156],"structural":[157],"characteristics":[158],"mixed.":[160],"Finally,":[161],"as":[162],"example":[164],"application":[167],"real":[172],"data,":[174],"high":[179],"resolution":[180],"acquired":[182],"DLR":[185],"E-SAR":[186],"sensor":[187],"L-band":[189],"illustrated.":[191]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}