{"id":"https://openalex.org/W3128031893","doi":"https://doi.org/10.3390/rs13030492","title":"Landscape Similarity Analysis Using Texture Encoded Deep-Learning Features on Unclassified Remote Sensing Imagery","display_name":"Landscape Similarity Analysis Using Texture Encoded Deep-Learning Features on Unclassified Remote Sensing Imagery","publication_year":2021,"publication_date":"2021-01-30","ids":{"openalex":"https://openalex.org/W3128031893","doi":"https://doi.org/10.3390/rs13030492","mag":"3128031893"},"language":"en","primary_location":{"id":"doi:10.3390/rs13030492","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13030492","pdf_url":"https://www.mdpi.com/2072-4292/13/3/492/pdf?version=1612174040","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/13/3/492/pdf?version=1612174040","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015497344","display_name":"Karim Malik","orcid":"https://orcid.org/0000-0002-9521-0523"},"institutions":[{"id":"https://openalex.org/I75381157","display_name":"Wilfrid Laurier University","ror":"https://ror.org/00fn7gb05","country_code":"CA","type":"education","lineage":["https://openalex.org/I75381157"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Karim Malik","raw_affiliation_strings":["Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada","institution_ids":["https://openalex.org/I75381157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041181617","display_name":"Colin Robertson","orcid":"https://orcid.org/0000-0003-0998-2971"},"institutions":[{"id":"https://openalex.org/I75381157","display_name":"Wilfrid Laurier University","ror":"https://ror.org/00fn7gb05","country_code":"CA","type":"education","lineage":["https://openalex.org/I75381157"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Colin Robertson","raw_affiliation_strings":["Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada","institution_ids":["https://openalex.org/I75381157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5015497344"],"corresponding_institution_ids":["https://openalex.org/I75381157"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":1.2633,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.81552533,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"13","issue":"3","first_page":"492","last_page":"492"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.998199999332428,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9961000084877014,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8446915745735168},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7968165874481201},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7898789644241333},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7275521755218506},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.703011155128479},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.49042651057243347},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.47714635729789734},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45441824197769165},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4258642792701721},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42547258734703064},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4218185842037201},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.41979360580444336},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33024609088897705},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3032032549381256},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11668127775192261}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8446915745735168},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7968165874481201},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7898789644241333},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7275521755218506},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.703011155128479},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.49042651057243347},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.47714635729789734},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45441824197769165},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4258642792701721},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42547258734703064},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4218185842037201},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.41979360580444336},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33024609088897705},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3032032549381256},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11668127775192261},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs13030492","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13030492","pdf_url":"https://www.mdpi.com/2072-4292/13/3/492/pdf?version=1612174040","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:5524a5b0d80e432798375b93fd016e8b","is_oa":true,"landing_page_url":"https://doaj.org/article/5524a5b0d80e432798375b93fd016e8b","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 13, Iss 3, p 492 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/13/3/492/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs13030492","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 13; Issue 3; Pages: 492","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs13030492","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13030492","pdf_url":"https://www.mdpi.com/2072-4292/13/3/492/pdf?version=1612174040","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6700000166893005,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G2165548363","display_name":null,"funder_award_id":"Canada","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"},{"id":"https://openalex.org/G8284766523","display_name":null,"funder_award_id":"(NSERC)","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"}],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3128031893.pdf","grobid_xml":"https://content.openalex.org/works/W3128031893.grobid-xml"},"referenced_works_count":80,"referenced_works":["https://openalex.org/W345900524","https://openalex.org/W819977924","https://openalex.org/W1491705651","https://openalex.org/W1524680991","https://openalex.org/W1628899544","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1915485278","https://openalex.org/W1960777822","https://openalex.org/W1981527205","https://openalex.org/W1997283462","https://openalex.org/W2006047816","https://openalex.org/W2028880308","https://openalex.org/W2063548203","https://openalex.org/W2095410437","https://openalex.org/W2143668817","https://openalex.org/W2204975001","https://openalex.org/W2253590344","https://openalex.org/W2295107390","https://openalex.org/W2464416094","https://openalex.org/W2470803522","https://openalex.org/W2515866431","https://openalex.org/W2561238782","https://openalex.org/W2585702364","https://openalex.org/W2604540523","https://openalex.org/W2605756230","https://openalex.org/W2608596745","https://openalex.org/W2613096586","https://openalex.org/W2616755213","https://openalex.org/W2621526417","https://openalex.org/W2665599524","https://openalex.org/W2715220489","https://openalex.org/W2747165560","https://openalex.org/W2755036008","https://openalex.org/W2755090963","https://openalex.org/W2766899523","https://openalex.org/W2771245298","https://openalex.org/W2776984690","https://openalex.org/W2782266583","https://openalex.org/W2789619030","https://openalex.org/W2792059834","https://openalex.org/W2792857687","https://openalex.org/W2794891691","https://openalex.org/W2795674590","https://openalex.org/W2799466885","https://openalex.org/W2810451272","https://openalex.org/W2883111419","https://openalex.org/W2886956694","https://openalex.org/W2887361581","https://openalex.org/W2902634115","https://openalex.org/W2912361013","https://openalex.org/W2913323966","https://openalex.org/W2913409447","https://openalex.org/W2921357635","https://openalex.org/W2941785734","https://openalex.org/W2945385604","https://openalex.org/W2945503248","https://openalex.org/W2946481693","https://openalex.org/W2946591776","https://openalex.org/W2957192340","https://openalex.org/W2961121772","https://openalex.org/W2962858109","https://openalex.org/W2963764690","https://openalex.org/W2963934397","https://openalex.org/W2964269821","https://openalex.org/W2964383635","https://openalex.org/W2967457289","https://openalex.org/W2971980344","https://openalex.org/W2974336699","https://openalex.org/W2982441053","https://openalex.org/W2998002262","https://openalex.org/W3011801827","https://openalex.org/W3041608560","https://openalex.org/W3044295292","https://openalex.org/W3096444215","https://openalex.org/W3103410140","https://openalex.org/W3105220622","https://openalex.org/W3105577662","https://openalex.org/W6743837088","https://openalex.org/W6762906906"],"related_works":["https://openalex.org/W2761785940","https://openalex.org/W2129933262","https://openalex.org/W3176438653","https://openalex.org/W2981628807","https://openalex.org/W3012393889","https://openalex.org/W3189091156","https://openalex.org/W4386087993","https://openalex.org/W3014041368","https://openalex.org/W4285815841","https://openalex.org/W3193641238"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"(CNNs)":[3],"are":[4,64,254],"known":[5],"for":[6,16,35,56,80,126,141,167,208,259],"their":[7,314],"ability":[8],"to":[9,220,345],"learn":[10,30],"shape":[11],"and":[12,21,70,90,113,135,144,165,170,176,186,256,279,299,312],"texture":[13,115],"descriptors":[14,295],"useful":[15],"object":[17,38],"detection,":[18],"pattern":[19,42,252],"recognition,":[20],"classification":[22,179],"problems.":[23],"Deeper":[24],"layer":[25,368],"filters":[26],"of":[27,130,137,157,223,268,297,308,328,366],"CNN":[28,102,119,175,238,291,367],"generally":[29],"global":[31],"image":[32,60,139],"information":[33,47,55,253],"vital":[34],"whole-scene":[36],"or":[37,196],"discrimination.":[39],"In":[40,92,230],"landscape":[41,261,301,323,342,372],"comparison,":[43],"however,":[44],"dense":[45],"localized":[46,76],"encoded":[48],"in":[49,67,111,181,351,371,378],"shallow":[50],"layers":[51],"can":[52],"contain":[53,250],"discriminative":[54,224,251,266],"characterizing":[57,298],"changes":[58],"across":[59,88],"local":[61],"regions":[62],"but":[63,212],"often":[65],"lost":[66],"the":[68,128,138,149,160,177,182,227,233,236,241,265,269,284,304,352,364],"deeper":[69],"non-spatial":[71],"fully":[72],"connected":[73],"layers.":[74],"Such":[75],"features":[77,225],"hold":[78],"potential":[79],"identifying,":[81],"as":[82,84,123,375,377],"well":[83,376],"characterizing,":[85],"process\u2013pattern":[86],"change":[87,379],"space":[89],"time.":[91],"this":[93,357],"paper,":[94],"we":[95,152,272],"propose":[96],"a":[97,105,124,154],"simple":[98],"yet":[99],"effective":[100],"texture-based":[101],"(Tex-CNN)":[103],"via":[104],"feature":[106,270,292,305,369],"concatenation":[107],"framework":[108],"which":[109,249],"results":[110],"capturing":[112],"learning":[114],"descriptors.":[116],"The":[117,173],"traditional":[118],"architecture":[120],"was":[121,191],"adopted":[122],"baseline":[125],"assessing":[127],"performance":[129,207],"Tex-CNN.":[131],"We":[132,288,355],"utilized":[133],"75%":[134],"25%":[136],"data":[140,204],"model":[142,168],"training":[143],"validation,":[145],"respectively.":[146,188],"To":[147,263],"test":[148],"models\u2019":[150],"generalization,":[151],"used":[153],"separate":[155],"set":[156],"imagery":[158],"from":[159],"Aerial":[161],"Imagery":[162],"Dataset":[163],"(AID)":[164],"Sentinel-2":[166,203],"development":[169],"independent":[171],"validation.":[172],"classical":[174,237],"Tex-CNN":[178,189,234],"accuracies":[180],"AID":[183],"were":[184],"91.67%":[185],"96.33%,":[187],"accuracy":[190],"either":[192],"on":[193,202],"par":[194],"with":[195,283,325],"outcompeted":[197],"state-of-the-art":[198],"methods.":[199],"Independent":[200],"validation":[201],"had":[205,213],"good":[206],"most":[209],"scene":[210,330],"types":[211,324,343],"difficulty":[214],"discriminating":[215],"farm":[216],"scenes,":[217],"likely":[218],"due":[219],"geometric":[221],"generalization":[222],"at":[226],"coarser":[228],"scale.":[229],"both":[231],"datasets,":[232],"outperformed":[235],"architecture.":[239],"Using":[240,303],"Tex-CNN,":[242],"gradient-based":[243],"spatial":[244,275],"attention":[245],"maps":[246,282,293,306,370],"(feature":[247],"maps)":[248],"extracted":[255],"subsequently":[257],"employed":[258],"mapping":[260],"similarity.":[262],"enhance":[264],"capacity":[267],"maps,":[271],"further":[273,361],"perform":[274],"filtering,":[276],"using":[277],"PCA":[278],"select":[280],"eigen":[281,286],"top":[285],"values.":[287],"show":[289,346],"that":[290],"provide":[294],"capable":[296],"quantifying":[300],"(dis)similarity.":[302],"histogram":[307],"oriented":[309],"gradient":[310],"vectors":[311],"computing":[313],"Earth":[315,334],"Movers":[316,335],"Distances,":[317],"our":[318],"method":[319],"effectively":[320],"identified":[321],"similar":[322],"over":[326],"60%":[327],"target-reference":[329],"comparisons":[331],"showing":[332],"smaller":[333],"Distance":[336],"(EMD)":[337],"(e.g.,":[338,349],"0.01),":[339],"while":[340],"different":[341],"tended":[344],"large":[347],"EMD":[348],"0.05)":[350],"benchmark":[353],"AID.":[354],"hope":[356],"proposal":[358],"will":[359],"inspire":[360],"research":[362],"into":[363],"use":[365],"similarity":[373],"assessment,":[374],"detection.":[380]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
