{"id":"https://openalex.org/W4313327554","doi":"https://doi.org/10.3390/rs15010170","title":"The Impact of the Type and Spatial Resolution of a Source Image on the Effectiveness of Texture Analysis","display_name":"The Impact of the Type and Spatial Resolution of a Source Image on the Effectiveness of Texture Analysis","publication_year":2022,"publication_date":"2022-12-28","ids":{"openalex":"https://openalex.org/W4313327554","doi":"https://doi.org/10.3390/rs15010170"},"language":"en","primary_location":{"id":"doi:10.3390/rs15010170","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15010170","pdf_url":"https://www.mdpi.com/2072-4292/15/1/170/pdf?version=1672831270","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/2072-4292/15/1/170/pdf?version=1672831270","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077537628","display_name":"Przemys\u0142aw Kupidura","orcid":"https://orcid.org/0000-0001-5506-7024"},"institutions":[{"id":"https://openalex.org/I108403487","display_name":"Warsaw University of Technology","ror":"https://ror.org/00y0xnp53","country_code":"PL","type":"education","lineage":["https://openalex.org/I108403487"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Przemys\u0142aw Kupidura","raw_affiliation_strings":["Faculty of Geodesy and Cartography, Warsaw University of Technology, 00-661 Warszawa, Poland"],"affiliations":[{"raw_affiliation_string":"Faculty of Geodesy and Cartography, Warsaw University of Technology, 00-661 Warszawa, Poland","institution_ids":["https://openalex.org/I108403487"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013283968","display_name":"Katarzyna Lesisz","orcid":null},"institutions":[{"id":"https://openalex.org/I108403487","display_name":"Warsaw University of Technology","ror":"https://ror.org/00y0xnp53","country_code":"PL","type":"education","lineage":["https://openalex.org/I108403487"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Katarzyna Lesisz","raw_affiliation_strings":["Faculty of Geodesy and Cartography, Warsaw University of Technology, 00-661 Warszawa, Poland"],"affiliations":[{"raw_affiliation_string":"Faculty of Geodesy and Cartography, Warsaw University of Technology, 00-661 Warszawa, Poland","institution_ids":["https://openalex.org/I108403487"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5077537628"],"corresponding_institution_ids":["https://openalex.org/I108403487"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.55,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.70127505,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":"15","issue":"1","first_page":"170","last_page":"170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9968000054359436,"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":0.9968000054359436,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9934999942779541,"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":0.986299991607666,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6123571991920471},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.5771558284759521},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.5501096248626709},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5320680141448975},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5255521535873413},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5059410929679871},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.48284393548965454},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4431685507297516},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4389348030090332},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.43191057443618774},{"id":"https://openalex.org/keywords/vegetation","display_name":"Vegetation (pathology)","score":0.4314194917678833},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40302133560180664},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34297695755958557},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.3413504660129547},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2946336269378662},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2376939356327057},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.2032393217086792}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6123571991920471},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.5771558284759521},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.5501096248626709},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5320680141448975},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5255521535873413},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5059410929679871},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.48284393548965454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4431685507297516},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4389348030090332},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.43191057443618774},{"id":"https://openalex.org/C2776133958","wikidata":"https://www.wikidata.org/wiki/Q7918366","display_name":"Vegetation (pathology)","level":2,"score":0.4314194917678833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40302133560180664},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34297695755958557},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3413504660129547},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2946336269378662},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2376939356327057},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.2032393217086792},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs15010170","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15010170","pdf_url":"https://www.mdpi.com/2072-4292/15/1/170/pdf?version=1672831270","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:8804c65acd2f4bd89e23b01ace858262","is_oa":true,"landing_page_url":"https://doaj.org/article/8804c65acd2f4bd89e23b01ace858262","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 15, Iss 1, p 170 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/1/170/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15010170","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 15; Issue 1; Pages: 170","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15010170","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15010170","pdf_url":"https://www.mdpi.com/2072-4292/15/1/170/pdf?version=1672831270","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":0.6800000071525574,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4313327554.pdf","grobid_xml":"https://content.openalex.org/works/W4313327554.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W113355003","https://openalex.org/W115219909","https://openalex.org/W133259164","https://openalex.org/W166169177","https://openalex.org/W1494720687","https://openalex.org/W1984792953","https://openalex.org/W1991360699","https://openalex.org/W1998538617","https://openalex.org/W2008755980","https://openalex.org/W2028278285","https://openalex.org/W2044465660","https://openalex.org/W2078180904","https://openalex.org/W2106587382","https://openalex.org/W2108094322","https://openalex.org/W2117395697","https://openalex.org/W2120027856","https://openalex.org/W2120587770","https://openalex.org/W2121308557","https://openalex.org/W2124571274","https://openalex.org/W2125027853","https://openalex.org/W2127199143","https://openalex.org/W2132984323","https://openalex.org/W2134893701","https://openalex.org/W2138829037","https://openalex.org/W2144362041","https://openalex.org/W2159268725","https://openalex.org/W2246640948","https://openalex.org/W2295193135","https://openalex.org/W2463238921","https://openalex.org/W2741876794","https://openalex.org/W2809635958","https://openalex.org/W2912961521","https://openalex.org/W2946670873","https://openalex.org/W2979416352","https://openalex.org/W3163456103","https://openalex.org/W4295799372","https://openalex.org/W4307726656","https://openalex.org/W6675875776","https://openalex.org/W6719124172"],"related_works":["https://openalex.org/W2011962637","https://openalex.org/W2540644541","https://openalex.org/W2163677711","https://openalex.org/W2036727360","https://openalex.org/W2044270176","https://openalex.org/W2615806692","https://openalex.org/W2080965330","https://openalex.org/W2374828682","https://openalex.org/W2547534968","https://openalex.org/W2153116791"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,60],"study":[4,27,65,126],"on":[5,16,101],"the":[6,17,23,29,67,70,74,131,143,146,174,177,187,192,198,203,208,223,244,252,256,259,264,267,274,277],"effectiveness":[7,117],"of":[8,11,22,49,69,73,104,121,133,140,148,166,168,179,181,186,190,202,210,239,254,258,269,276],"texture":[9,95,141,150,169,248],"analysis":[10,96,99],"remote":[12],"sensing":[13],"imagery":[14],"depending":[15],"type":[18,135,238],"and":[19,43,136,145],"spatial":[20,200,225],"resolution":[21,137,201,226],"source":[24],"image.":[25,63],"The":[26,64,91,124,155],"used":[28,93],"following":[30,75],"image":[31,134,144,204,240,257,275],"types:":[32],"near-infrared":[33],"band,":[34,36],"red":[35,278],"first":[37,260],"principal":[38,41,261],"component,":[39],"second":[40],"component":[42,262],"normalized":[44],"difference":[45],"vegetation":[46],"index":[47],"images":[48],"pixel":[50],"size":[51],"from":[52,59,176],"2":[53],"m":[54],"to":[55,129,151],"30":[56],"m,":[57],"generated":[58],"multispectral":[61],"WorldView-2":[62],"evaluated":[66],"separability":[68],"selected":[71],"pairs":[72,189],"land":[76],"cover":[77],"classes:":[78],"bare":[79],"soil,":[80],"low":[81],"vegetation,":[82],"coniferous":[83],"forest,":[84,86],"deciduous":[85],"water":[87],"reservoirs,":[88],"built-up":[89,211,270],"areas.":[90],"tool":[92],"for":[94,138,197,247,251,266],"was":[97,220,263,273],"granulometric":[98],"based":[100],"morphological":[102],"operations\u2014one":[103],"less":[105],"popular":[106],"methods":[107],"which,":[108],"however,":[109],"as":[110],"demonstrated":[111],"by":[112],"previous":[113],"studies,":[114],"shows":[115],"high":[116],"in":[118,142,215],"separating":[119],"classes":[120,255],"different":[122],"texture.":[123],"conducted":[125],"enabled":[127],"researchers":[128],"evaluate":[130],"significance":[132],"visibility":[139],"possibility":[147],"using":[149],"differentiate":[152],"between":[153],"classes.":[154,183],"obtained":[156,196,221],"results":[157,194],"showed":[158],"that":[159,233,241],"there":[160,234],"is":[161,235,242],"no":[162,236],"single,":[163],"universal":[164],"combination":[165],"conditions":[167],"analysis,":[170],"which":[171],"would":[172],"be":[173],"best":[175,193,218,245],"point":[178],"view":[180],"all":[182],"For":[184],"most":[185],"analyzed":[188],"classes,":[191],"were":[195],"highest":[199],"(2\u20133":[205],"m),":[206],"but":[207],"class":[209,268],"areas":[212,271],"stands":[213],"out":[214],"this":[216],"comparison\u2014the":[217],"distinction":[219],"with":[222],"average":[224],"(10\u201315":[227],"m).":[228],"Research":[229],"has":[230],"also":[231],"shown":[232],"single":[237],"universally":[243],"basis":[246],"analysis.":[249],"While":[250],"majority":[253],"best,":[265],"it":[272],"channel.":[279]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
