{"id":"https://openalex.org/W2547640685","doi":"https://doi.org/10.1109/igarss.2016.7730799","title":"Fast topology preserving PolSAR image superpixel segmentation","display_name":"Fast topology preserving PolSAR image superpixel segmentation","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2547640685","doi":"https://doi.org/10.1109/igarss.2016.7730799","mag":"2547640685"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2016.7730799","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7730799","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 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/A5027639981","display_name":"Weiwei Guo","orcid":"https://orcid.org/0000-0001-5037-0972"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiwei Guo","raw_affiliation_strings":["Shanghai Key Lab. of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Lab. of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059094652","display_name":"Zenghui Zhang","orcid":"https://orcid.org/0000-0002-1238-8538"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zenghui Zhang","raw_affiliation_strings":["Shanghai Key Lab. of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Lab. of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064605829","display_name":"Juanping Zhao","orcid":"https://orcid.org/0000-0002-1620-4580"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juanping Zhao","raw_affiliation_strings":["Shanghai Key Lab. of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Lab. of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100445293","display_name":"Wenxian Yu","orcid":"https://orcid.org/0000-0002-8741-776X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxian Yu","raw_affiliation_strings":["Shanghai Key Lab. of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Lab. of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5027639981"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.167,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58104956,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"6894","last_page":"6897"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9980999827384949,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9980999827384949,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9951000213623047,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9919000267982483,"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":0.7482771873474121},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7150171995162964},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6182892322540283},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5836161375045776},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5610640048980713},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5573253631591797},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.516244649887085},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5109607577323914},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.486054927110672},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45699259638786316},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.44861993193626404},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34931159019470215},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1977965235710144}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7482771873474121},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7150171995162964},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6182892322540283},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5836161375045776},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5610640048980713},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5573253631591797},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.516244649887085},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5109607577323914},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.486054927110672},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45699259638786316},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.44861993193626404},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34931159019470215},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1977965235710144},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2016.7730799","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7730799","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1950112384","https://openalex.org/W1994938019","https://openalex.org/W2002466026","https://openalex.org/W2073842936","https://openalex.org/W2118246710","https://openalex.org/W2121947440"],"related_works":["https://openalex.org/W1978572805","https://openalex.org/W2383807498","https://openalex.org/W4233585817","https://openalex.org/W2016045932","https://openalex.org/W1675950995","https://openalex.org/W2188882668","https://openalex.org/W2004379491","https://openalex.org/W2088323302","https://openalex.org/W2083140487","https://openalex.org/W1995025910"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,15,22,47,91],"fast":[6],"PolSAR":[7],"image":[8,33],"superpixel":[9],"segmentation":[10],"method.":[11],"This":[12],"method":[13],"takes":[14],"simple":[16],"coarse-to-fine":[17],"optimization":[18],"technique":[19],"to":[20,54],"minimize":[21],"Markov-Random-Field":[23],"(MRF)":[24],"like":[25],"energy":[26],"function":[27],"which":[28],"integrates":[29],"the":[30,55,61,68],"Pol-":[31],"SAR":[32],"statistic,":[34],"spatial":[35],"position":[36],"and":[37,51,70,89,96],"boundary":[38,42],"smoothing.":[39],"It":[40],"updates":[41],"of":[43,63],"superpixels":[44],"staring":[45],"with":[46],"large":[48],"block":[49],"level":[50],"iterates":[52],"down":[53],"final":[56],"pixel":[57],"level.":[58],"We":[59],"demonstrate":[60],"performance":[62],"our":[64,78],"approach":[65,80],"both":[66],"on":[67],"synthetic":[69],"real":[71],"full":[72],"polarimetric":[73],"images":[74],",":[75],"showing":[76],"that":[77],"proposed":[79],"can":[81],"achieve":[82],"significantly":[83],"faster":[84],"convergence":[85],"than":[86],"SLIC":[87],"method,":[88],"make":[90],"good":[92],"compromise":[93],"between":[94],"accuracy":[95],"computation":[97],"speed.":[98]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}