{"id":"https://openalex.org/W2149464962","doi":"https://doi.org/10.1109/igarss.2007.4423880","title":"Identification of inland fresh water wetland using SAR and ETM+ data","display_name":"Identification of inland fresh water wetland using SAR and ETM+ data","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W2149464962","doi":"https://doi.org/10.1109/igarss.2007.4423880","mag":"2149464962"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2007.4423880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2007.4423880","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 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/A5102162416","display_name":"Renzong Ruan","orcid":null},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]},{"id":"https://openalex.org/I4210099662","display_name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","ror":"https://ror.org/012wsxz85","country_code":"CN","type":"facility","lineage":["https://openalex.org/I163340411","https://openalex.org/I4210099662","https://openalex.org/I4210111986","https://openalex.org/I4210120069","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Renzong Ruan","raw_affiliation_strings":["State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Hohai University, HHU, Nanjing, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Hohai University, HHU, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I4210099662","https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023528036","display_name":"Liliang Ren","orcid":"https://orcid.org/0000-0002-3329-5787"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]},{"id":"https://openalex.org/I4210099662","display_name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","ror":"https://ror.org/012wsxz85","country_code":"CN","type":"facility","lineage":["https://openalex.org/I163340411","https://openalex.org/I4210099662","https://openalex.org/I4210111986","https://openalex.org/I4210120069","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liliang Ren","raw_affiliation_strings":["State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Hohai University, HHU, Nanjing, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Hohai University, HHU, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I4210099662","https://openalex.org/I163340411"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102162416"],"corresponding_institution_ids":["https://openalex.org/I163340411","https://openalex.org/I4210099662"],"apc_list":null,"apc_paid":null,"fwci":0.7503,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77980434,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"52","issue":null,"first_page":"4592","last_page":"4595"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T13282","display_name":"Automated Road and Building Extraction","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10930","display_name":"Flood Risk Assessment and Management","score":0.9805999994277954,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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":0.9448999762535095,"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/wetland","display_name":"Wetland","score":0.8032052516937256},{"id":"https://openalex.org/keywords/watershed","display_name":"Watershed","score":0.6049875020980835},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.5933969020843506},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5517808198928833},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5213730931282043},{"id":"https://openalex.org/keywords/paddy-field","display_name":"Paddy field","score":0.4740923345088959},{"id":"https://openalex.org/keywords/hydrology","display_name":"Hydrology (agriculture)","score":0.46522969007492065},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.41823670268058777},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.26984652876853943},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.2181049883365631},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.21585282683372498},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17916011810302734},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.13097110390663147},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.10560542345046997}],"concepts":[{"id":"https://openalex.org/C67715294","wikidata":"https://www.wikidata.org/wiki/Q170321","display_name":"Wetland","level":2,"score":0.8032052516937256},{"id":"https://openalex.org/C150547873","wikidata":"https://www.wikidata.org/wiki/Q947851","display_name":"Watershed","level":2,"score":0.6049875020980835},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5933969020843506},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5517808198928833},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5213730931282043},{"id":"https://openalex.org/C85582077","wikidata":"https://www.wikidata.org/wiki/Q842623","display_name":"Paddy field","level":2,"score":0.4740923345088959},{"id":"https://openalex.org/C76886044","wikidata":"https://www.wikidata.org/wiki/Q2883300","display_name":"Hydrology (agriculture)","level":2,"score":0.46522969007492065},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.41823670268058777},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.26984652876853943},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.2181049883365631},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.21585282683372498},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17916011810302734},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.13097110390663147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.10560542345046997},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2007.4423880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2007.4423880","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.5799999833106995,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W62647272","https://openalex.org/W1523879065","https://openalex.org/W1980989386","https://openalex.org/W2033697548","https://openalex.org/W2066595076","https://openalex.org/W2075505566","https://openalex.org/W2096611296","https://openalex.org/W2111975408","https://openalex.org/W2124624125","https://openalex.org/W2154717921","https://openalex.org/W2159429463","https://openalex.org/W2186263394","https://openalex.org/W2189507257","https://openalex.org/W2515342364","https://openalex.org/W4214564766","https://openalex.org/W4285719527","https://openalex.org/W6602601728","https://openalex.org/W6631361901","https://openalex.org/W6686447371","https://openalex.org/W6687106723"],"related_works":["https://openalex.org/W2381193838","https://openalex.org/W2383007022","https://openalex.org/W1995429609","https://openalex.org/W2367678540","https://openalex.org/W2376642692","https://openalex.org/W1992980705","https://openalex.org/W2388744186","https://openalex.org/W2355754934","https://openalex.org/W1504540281","https://openalex.org/W2390294057"],"abstract_inverted_index":{"The":[0,32,147,291,302,329],"main":[1],"aim":[2],"of":[3,11,38,52,64,74,93,105,143,165,176,180,188,191,200,204,207,224,248,263,268,308,325,335,341,360],"this":[4],"paper":[5],"was":[6,88,111,154,243,258,278,346,351],"to":[7,61,114,260],"explore":[8],"the":[9,41,67,91,100,103,106,139,171,174,184,214,222,228,236,246,261,266,271,306,309,313,320,333,339,358],"potential":[10],"SAR":[12,276,310],"data,":[13,20,210],"in":[14,21,47,58,99,127,270,338],"combination":[15,307],"with":[16],"optical":[17,314],"remote":[18],"sensing":[19],"identifying":[22],"inland":[23],"fresh":[24,44,342],"water":[25,45,343],"wetland":[26,65,85,94],"from":[27,293],"crop,":[28],"especially":[29],"rice":[30],"paddy.":[31],"test":[33],"area":[34],"is":[35,50,69],"a":[36,71,352],"part":[37],"Hongze":[39,77,83],"Lake,":[40],"fourth":[42],"biggest":[43],"lake":[46,68,78],"China.":[48,59],"It":[49,345],"one":[51],"important":[53],"wetlands":[54],"for":[55,90,119,168,173,221,265,357],"migratory":[56],"birds":[57],"Due":[60],"unreasonable":[62],"exploitation":[63],"resources,":[66],"facing":[70],"great":[72],"loss":[73],"wetland.":[75,344,361],"In":[76,102],"watershed,":[79],"Jiangsu":[80],"Provincial":[81],"Sihong":[82],"Lake":[84],"ecological":[86],"reserve":[87],"established":[89],"preserve":[92],"ecosystem":[95],"and":[96,160,170,197,217,288,312],"rare":[97],"species":[98],"watershed.":[101],"processing":[104],"dataset,":[107],"clustering":[108],"algorithm":[109,220],"ISODATA":[110],"employed":[112],"firstly":[113],"generate":[115],"initial":[116],"classification":[117,177,267,322,327,336],"results":[118,292,303,330],"sample":[120],"selection.":[121],"Then,":[122,254],"1500":[123],"samples":[124,135,164,181],"were":[125,136,167,211,231,297],"taken":[126],"total":[128,326],"by":[129,245,299],"using":[130,300],"stratified":[131],"random":[132],"sampling.":[133],"These":[134],"superimposed":[137],"on":[138,141,157],"screen":[140],"top":[142],"rectified":[144],"aerial":[145],"images.":[146],"land":[148],"cover":[149],"class":[150,237],"at":[151,239,251],"each":[152,240],"point":[153],"determined":[155,244],"based":[156],"field":[158],"investigation":[159],"visual":[161],"interpretation.":[162],"900":[163],"them":[166],"training":[169,229],"other":[172],"assessment":[175],"accuracy.":[178],"Attributes":[179],"such":[182],"as":[183],"digital":[185],"number":[186],"values":[187],"six":[189,205],"bands":[190,206],"ETM+(":[192],"TM1-5,":[193],"7),":[194],"texture,":[195],"DEM":[196],"4":[198],"components":[199,202],"principal":[201],"analysis":[203],"ETM":[208],"+":[209],"fed":[212],"into":[213,280],"CART":[215],"(Classification":[216],"Regression":[218],"Tree)":[219],"generation":[223],"knowledge":[225],"rules.":[226,301],"Because":[227],"observations":[230,250],"evenly":[232],"distributed":[233],"among":[234],"classes,":[235],"assignment":[238],"terminal":[241],"node":[242],"majority":[247],"per-class":[249],"that":[252,305,348],"node.":[253],"decision":[255],"tree":[256,337],"classifier":[257],"applied":[259],"imagery":[262],"ETM+":[264],"landuse/cover":[269],"whole":[272],"study":[273],"area.":[274],"RADARSAT":[275],"C-band":[277],"classified":[279],"four":[281],"classes:":[282],"lowest":[283],"backscatter,":[284,286],"low":[285],"medium":[287],"high":[289],"backscatter.":[290],"two":[294],"data":[295,311,317,350,355],"sources":[296],"combined":[298],"showed":[304],"remotely":[315],"sensed":[316],"have":[318],"achieved":[319],"highest":[321],"accuracy":[323],"(92.3%":[324],"accuracy).":[328],"also":[331],"confirmed":[332],"value":[334],"identification":[340,359],"illustrated":[347],"radar":[349],"good":[353],"complementary":[354],"source":[356]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
