{"id":"https://openalex.org/W2771450566","doi":"https://doi.org/10.1109/igarss.2017.8127858","title":"Polsar image classification based on three-dimensional wavelet texture features and Markov random field","display_name":"Polsar image classification based on three-dimensional wavelet texture features and Markov random field","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2771450566","doi":"https://doi.org/10.1109/igarss.2017.8127858","mag":"2771450566"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2017.8127858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5039966032","display_name":"Haixia Bi","orcid":"https://orcid.org/0009-0003-2879-0550"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haixia Bi","raw_affiliation_strings":["Institute for Information and System Sciences, Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Institute for Information and System Sciences, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100440530","display_name":"Lin Xu","orcid":"https://orcid.org/0000-0003-4373-0591"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lin Xu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, New York University, Abu Dhabi, UAE"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, New York University, Abu Dhabi, UAE","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028103486","display_name":"Xiangyong Cao","orcid":"https://orcid.org/0000-0001-7912-3457"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyong Cao","raw_affiliation_strings":["Institute for Information and System Sciences, Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Institute for Information and System Sciences, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109280540","display_name":"Zongben Xu","orcid":"https://orcid.org/0000-0002-4066-2338"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongben Xu","raw_affiliation_strings":["Institute for Information and System Sciences, Xi'an Jiaotong University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Institute for Information and System Sciences, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5039966032"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":6.5326,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.96223968,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3921","last_page":"3928"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9972000122070312,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9969000220298767,"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":0.810583770275116},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7642273902893066},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.7566580772399902},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6738032102584839},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5675150752067566},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.562779426574707},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.555087149143219},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4626181423664093},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.4445815086364746},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4324406683444977},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.4207446277141571},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.4163154661655426},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.41561439633369446},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.40044069290161133},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3917899429798126},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.325602650642395},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.24389135837554932},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.23655155301094055}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.810583770275116},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7642273902893066},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.7566580772399902},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6738032102584839},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5675150752067566},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.562779426574707},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.555087149143219},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4626181423664093},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.4445815086364746},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4324406683444977},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.4207446277141571},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.4163154661655426},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.41561439633369446},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.40044069290161133},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3917899429798126},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.325602650642395},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.24389135837554932},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.23655155301094055},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2017.8127858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2005126631","https://openalex.org/W2008826820","https://openalex.org/W2018775529","https://openalex.org/W2029316659","https://openalex.org/W2048578702","https://openalex.org/W2116246092","https://openalex.org/W2144554203","https://openalex.org/W2164296623","https://openalex.org/W2167309519","https://openalex.org/W2169282664","https://openalex.org/W2169551590","https://openalex.org/W2181938598","https://openalex.org/W2244824390","https://openalex.org/W2248623186","https://openalex.org/W2345852998","https://openalex.org/W2492018752","https://openalex.org/W2555840851","https://openalex.org/W3152188166"],"related_works":["https://openalex.org/W2793402697","https://openalex.org/W2967406116","https://openalex.org/W2042019967","https://openalex.org/W2093785611","https://openalex.org/W2373481280","https://openalex.org/W2015790018","https://openalex.org/W2134401318","https://openalex.org/W1555506570","https://openalex.org/W2147064750","https://openalex.org/W1966354130"],"abstract_inverted_index":{"The":[0],"speckle":[1],"effect":[2],"embedded":[3],"in":[4,32,70,95],"polarimetric":[5],"synthetic":[6],"aperture":[7],"radar":[8],"(PolSAR)":[9],"data":[10],"damages":[11],"the":[12,71,84,87,99],"performance":[13],"of":[14,86],"PolSAR":[15,91],"image":[16,92],"classification":[17,26,37,107],"greatly.":[18],"To":[19,82],"alleviate":[20],"this":[21,103],"issue,":[22],"a":[23,77],"new":[24],"supervised":[25],"method,":[27,89],"which":[28,54,73],"introduces":[29],"spatial":[30,112],"consistency":[31],"both":[33],"feature":[34],"extraction":[35],"and":[36,109],"steps":[38],"is":[39,47,68,74,93],"proposed.":[40],"Specifically,":[41],"three-dimensional":[42],"discrete":[43],"wavelet":[44],"transform":[45],"(3D-DWT)":[46],"used":[48,94],"to":[49,57],"extract":[50],"spectral-spatial":[51],"texture":[52],"features,":[53],"are":[55],"proved":[56],"be":[58],"more":[59],"discriminative":[60],"than":[61],"original":[62],"ones.":[63],"Afterward,":[64],"label":[65],"smoothness":[66],"prior":[67],"incorporated":[69],"classification,":[72],"implemented":[75],"using":[76],"Markov":[78],"random":[79],"field":[80],"(MRF).":[81],"demonstrate":[83],"validity":[85],"proposed":[88],"real":[90],"experiments.":[96],"Compared":[97],"with":[98],"other":[100],"state-of-the-art":[101],"methods,":[102],"method":[104],"achieves":[105],"higher":[106],"accuracy":[108],"better":[110],"visual":[111],"connectivity.":[113]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
