{"id":"https://openalex.org/W1592765591","doi":"https://doi.org/10.1109/igarss.2004.1369088","title":"Incorporating texture information into polarimetric radar classification using neural networks","display_name":"Incorporating texture information into polarimetric radar classification using neural networks","publication_year":2004,"publication_date":"2004-12-23","ids":{"openalex":"https://openalex.org/W1592765591","doi":"https://doi.org/10.1109/igarss.2004.1369088","mag":"1592765591"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2004.1369088","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2004.1369088","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/A5097582186","display_name":"K. Ersahin","orcid":null},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Bernd Scheuchl Kaan Ersahin","raw_affiliation_strings":["Dept. of Electr. & Comput. Eng., British Columbia Univ., Vancouver, BC, Canada"],"affiliations":[{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., British Columbia Univ., Vancouver, BC, Canada","institution_ids":["https://openalex.org/I141945490"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5097582186"],"corresponding_institution_ids":["https://openalex.org/I141945490"],"apc_list":null,"apc_paid":null,"fwci":39.0357,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.99238066,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"1","issue":null,"first_page":"560","last_page":"563"},"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":0.992900013923645,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9922000169754028,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7922745943069458},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7084668874740601},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.587225079536438},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.541650116443634},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5328261256217957},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5046688318252563},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.48565876483917236},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.47017163038253784},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.46346014738082886},{"id":"https://openalex.org/keywords/wishart-distribution","display_name":"Wishart distribution","score":0.46108147501945496},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4530392289161682},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.44222480058670044},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.4307973384857178},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1846296191215515},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15950846672058105},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10120758414268494}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7922745943069458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7084668874740601},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.587225079536438},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.541650116443634},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5328261256217957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5046688318252563},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.48565876483917236},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.47017163038253784},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.46346014738082886},{"id":"https://openalex.org/C33962027","wikidata":"https://www.wikidata.org/wiki/Q1930697","display_name":"Wishart distribution","level":3,"score":0.46108147501945496},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4530392289161682},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.44222480058670044},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.4307973384857178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1846296191215515},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15950846672058105},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10120758414268494},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss.2004.1369088","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2004.1369088","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"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.407.1926","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.407.1926","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://sar.ece.ubc.ca/papers/Ersahin_IGARSS_2004.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","score":0.550000011920929,"id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1985194020","https://openalex.org/W2001337397","https://openalex.org/W2059532376","https://openalex.org/W2060971099","https://openalex.org/W2101308399","https://openalex.org/W2127227873","https://openalex.org/W2129526018","https://openalex.org/W2130762895","https://openalex.org/W2144841545","https://openalex.org/W2149520480","https://openalex.org/W2804513110","https://openalex.org/W3016810848","https://openalex.org/W4205687621","https://openalex.org/W4245176872","https://openalex.org/W4255443310","https://openalex.org/W6751847443"],"related_works":["https://openalex.org/W2146312983","https://openalex.org/W2389605595","https://openalex.org/W1586339758","https://openalex.org/W2046861201","https://openalex.org/W3134381438","https://openalex.org/W2057690963","https://openalex.org/W2118598748","https://openalex.org/W2011956837","https://openalex.org/W2108933226","https://openalex.org/W2553651735"],"abstract_inverted_index":{"Most":[0],"of":[1,33,51,76],"the":[2,14,31,34,49,66,77,83,95,104],"recent":[3],"research":[4],"on":[5,10],"polarimetric":[6,25],"SAR":[7],"classification":[8,53,91,99],"focused":[9],"pixel-based":[11],"techniques":[12,28],"using":[13,65],"covariance":[15],"matrix":[16],"representation.":[17],"Since":[18],"multiple":[19],"channels":[20],"are":[21,101],"inherently":[22],"provided":[23],"in":[24,74],"data,":[26],"conventional":[27],"for":[29],"increasing":[30],"dimensionality":[32],"observation,":[35],"such":[36],"as":[37],"texture":[38,52],"feature":[39,78],"extraction,":[40],"were":[41],"ignored.":[42],"In":[43],"this":[44],"paper,":[45],"we":[46],"have":[47],"demonstrated":[48],"potential":[50],"through":[54],"gray":[55],"level":[56],"cooccurrence":[57],"probabilities":[58],"(GLCP),":[59],"and":[60,86],"proposed":[61],"an":[62],"unsupervised":[63],"scheme":[64],"self-organizing":[67],"map":[68],"(SOM)":[69],"neural":[70],"network.":[71],"The":[72],"increase":[73],"separability":[75],"space":[79],"is":[80],"shown":[81],"via":[82],"Fisher":[84],"criterion":[85],"also":[87],"verified":[88],"by":[89],"increased":[90],"performance.":[92],"Compared":[93],"to":[94],"Wishart":[96],"classifier,":[97],"promising":[98],"results":[100],"obtained":[102],"from":[103],"Flevoland":[105],"data":[106],"set.":[107]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
