{"id":"https://openalex.org/W1983759705","doi":"https://doi.org/10.1109/igarss.2014.6947079","title":"An efficient framework for spectral-spatial classification of hyperspectral images in urban areas","display_name":"An efficient framework for spectral-spatial classification of hyperspectral images in urban areas","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W1983759705","doi":"https://doi.org/10.1109/igarss.2014.6947079","mag":"1983759705"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2014.6947079","doi":"https://doi.org/10.1109/igarss.2014.6947079","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2014.6947079","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5074612097","display_name":"Davood Akbari","orcid":"https://orcid.org/0000-0001-8617-8093","observed_orcids":["https://orcid.org/0000-0001-8617-8093"]},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Davood Akbari","raw_affiliation_strings":["S Dept. of Surveying and Geomatics Eng., University of Tehran, Tehran, Iran","S Dept. of Surveying and Geomatics Eng., College of Engineering, University of Tehran, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"S Dept. of Surveying and Geomatics Eng., University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]},{"raw_affiliation_string":"S Dept. of Surveying and Geomatics Eng., College of Engineering, University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063724972","display_name":"Saeid Homayouni","orcid":"https://orcid.org/0000-0002-0214-5356","observed_orcids":["https://orcid.org/0000-0002-0214-5356"]},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Saeid Homayouni","raw_affiliation_strings":["Department of Geography, University of Ottawa, Ottawa, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography, University of Ottawa, Ottawa, Canada","institution_ids":["https://openalex.org/I153718931"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003182960","display_name":"Abdolreza Safari","orcid":"https://orcid.org/0000-0001-5938-5468","observed_orcids":["https://orcid.org/0000-0001-5938-5468"]},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Abdolreza Safari","raw_affiliation_strings":["S Dept. of Surveying and Geomatics Eng., University of Tehran, Tehran, Iran","S Dept. of Surveying and Geomatics Eng., College of Engineering, University of Tehran, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"S Dept. of Surveying and Geomatics Eng., University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]},{"raw_affiliation_string":"S Dept. of Surveying and Geomatics Eng., College of Engineering, University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028152226","display_name":"Safa Khazai","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I120234788","display_name":"Imam Hossein University","ror":"https://ror.org/04esb6v42","country_code":"IR","type":"education","lineage":["https://openalex.org/I120234788"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Safa Khazai","raw_affiliation_strings":["Dept. Civil Eng., Imam Hussein Comprehensive University, Tehran, Iran","Dept. Civil Eng., College of Engineering, Imam Hussein Comprehensive University, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. Civil Eng., Imam Hussein Comprehensive University, Tehran, Iran","institution_ids":[]},{"raw_affiliation_string":"Dept. Civil Eng., College of Engineering, Imam Hussein Comprehensive University, Tehran, Iran","institution_ids":["https://openalex.org/I120234788"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1751,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.35126582,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"2886","last_page":"2889"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.0,"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/hyperspectral-image-classification","display_name":"hyperspectral image classification","score":0.9729999899864197},{"id":"https://openalex.org/keywords/spectral-spatial-classification","display_name":"spectral-spatial classification","score":0.9399999976158142},{"id":"https://openalex.org/keywords/urban-environment","display_name":"urban environment","score":0.8489999771118164},{"id":"https://openalex.org/keywords/minimum-spanning-forest","display_name":"minimum spanning forest","score":0.9649999737739563},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.6480000019073486},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"principal component analysis","score":0.8320000171661377},{"id":"https://openalex.org/keywords/wavelet-features","display_name":"wavelet features","score":0.753000020980835},{"id":"https://openalex.org/keywords/gabor-filters","display_name":"Gabor filters","score":0.8510000109672546},{"id":"https://openalex.org/keywords/texture-features","display_name":"texture features","score":0.9480000138282776},{"id":"https://openalex.org/keywords/classification-accuracy","display_name":"classification accuracy","score":0.3190000057220459}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.942541241645813},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.7542093992233276},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7206860780715942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6744983196258545},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5873815417289734},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5608066320419312},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5580182075500488},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.43515923619270325},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41194993257522583},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3549521565437317},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3507998585700989},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.224726140499115},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1731869876384735},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2014.6947079","doi":"https://doi.org/10.1109/igarss.2014.6947079","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2014.6947079","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7250000238418579,"display_name":"Sustainable cities and communities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2043665634","https://openalex.org/W2044465660","https://openalex.org/W2078296814","https://openalex.org/W2093628091","https://openalex.org/W2104528508","https://openalex.org/W2115755118","https://openalex.org/W2160662337","https://openalex.org/W2611950832","https://openalex.org/W4256068510","https://openalex.org/W4320339642"],"related_works":["https://openalex.org/W3022901789","https://openalex.org/W2770269746","https://openalex.org/W2769613910","https://openalex.org/W2275733000","https://openalex.org/W2736425406","https://openalex.org/W2283858015","https://openalex.org/W2148791530","https://openalex.org/W2588217599","https://openalex.org/W2964989080","https://openalex.org/W2001298023"],"abstract_inverted_index":{"Many":[0],"researches":[1],"have":[2],"demonstrated":[3],"that":[4,107],"the":[5,14,61,68,73,110,114],"spatial":[6,63,79],"information":[7],"can":[8],"play":[9],"an":[10,20],"important":[11],"role":[12],"in":[13,51,82],"classification":[15,25,47,120],"of":[16,48,67],"hyperspectral":[17,49,70,104],"imagery.":[18],"Recently,":[19],"effective":[21],"approach":[22,45],"for":[23,46],"spectral-spatial":[24],"has":[26],"been":[27],"proposed":[28,55,115],"using":[29,72],"Minimum":[30],"Spanning":[31],"Forest":[32],"(MSF)":[33],"grown":[34],"from":[35],"automatically":[36],"selected":[37],"markers.":[38],"This":[39],"paper":[40],"aims":[41],"at":[42],"improving":[43],"this":[44,83],"images":[50],"urban":[52],"areas.":[53],"The":[54,78,99],"framework":[56,116],"is":[57],"based":[58],"on":[59,102],"deriving":[60],"optimal":[62],"and":[64,97],"spectral":[65],"features":[66,80],"original":[69,111],"image":[71],"Principal":[74],"Component":[75],"Analysis":[76],"(PCA).":[77],"extracted":[81],"study":[84],"are":[85],"wavelet,":[86],"Gabor":[87],"filter,":[88],"mean,":[89],"contrast,":[90],"entropy,":[91],"variance,":[92],"homogeneity,":[93],"dissimilarity,":[94],"second":[95],"moment,":[96],"correlation.":[98],"experimental":[100],"results":[101],"three":[103],"datasets":[105],"demonstrate":[106],"compared":[108],"to":[109],"MSF-based":[112],"approach,":[113],"yields":[117],"more":[118],"accurate":[119],"maps.":[121]},"counts_by_year":[{"year":2017,"cited_by_count":1}],"updated_date":"2026-10-27T15:51:10.671819","created_date":"2016-06-24T00:00:00"}