{"id":"https://openalex.org/W1972426703","doi":"https://doi.org/10.1109/igarss.2015.7326633","title":"Sparse modeling of the land use classification problem","display_name":"Sparse modeling of the land use classification problem","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W1972426703","doi":"https://doi.org/10.1109/igarss.2015.7326633","mag":"1972426703"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2015.7326633","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2015.7326633","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 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/A5078980808","display_name":"Mohamed Lamine Mekhalfi","orcid":"https://orcid.org/0000-0002-4295-0974"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Mohamed L. Mekhalfi","raw_affiliation_strings":["Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","Dept. of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]},{"raw_affiliation_string":"Dept. of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021389231","display_name":"Farid Melgani","orcid":"https://orcid.org/0000-0001-9745-3732"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Farid Melgani","raw_affiliation_strings":["Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","Dept. of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]},{"raw_affiliation_string":"Dept. of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5078980808"],"corresponding_institution_ids":["https://openalex.org/I193223587"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.07760881,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"8","issue":null,"first_page":"3727","last_page":"3730"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T10057","display_name":"Face and Expression Recognition","score":0.9886999726295471,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9882000088691711,"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/benchmark","display_name":"Benchmark (surveying)","score":0.8172321319580078},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.699120819568634},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6957910060882568},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6624182462692261},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6137539148330688},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5803397297859192},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5335527062416077},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.519871711730957},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5186876058578491},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49423840641975403},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.44392621517181396},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4158986210823059},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3612164258956909},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34214985370635986},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16485124826431274},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10142964124679565}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8172321319580078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.699120819568634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6957910060882568},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6624182462692261},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6137539148330688},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5803397297859192},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5335527062416077},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.519871711730957},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5186876058578491},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49423840641975403},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.44392621517181396},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4158986210823059},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3612164258956909},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34214985370635986},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16485124826431274},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10142964124679565},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss.2015.7326633","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2015.7326633","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"},{"id":"pmh:oai:iris.unitn.it:11572/154273","is_oa":false,"landing_page_url":"http://hdl.handle.net/11572/154273","pdf_url":null,"source":{"id":"https://openalex.org/S4306401913","display_name":"Institutional Research Information System (Universit\u00e0 degli Studi di Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.6200000047683716,"display_name":"Life in Land"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320333031","display_name":"University of California Merced","ror":"https://ror.org/00d9ah105"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1968591910","https://openalex.org/W1980038761","https://openalex.org/W1989409679","https://openalex.org/W2001123951","https://openalex.org/W2031061770","https://openalex.org/W2046658845","https://openalex.org/W2055085016","https://openalex.org/W2077689834","https://openalex.org/W2145096794","https://openalex.org/W2163352848","https://openalex.org/W6642202504"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2087343574","https://openalex.org/W2565656575","https://openalex.org/W4390143830"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"present":[4],"a":[5,10,38,53,59,79],"fusion":[6,54],"method":[7,84],"contextualized":[8],"within":[9],"land":[11],"use":[12],"classification":[13],"framework.":[14],"At":[15],"first,":[16],"feature":[17,42],"vectors":[18,34,43],"are":[19,35],"extracted":[20,44],"from":[21,45],"all":[22],"the":[23,27,32,66,70,74,82],"color":[24],"channels":[25],"of":[26,40,69],"given":[28],"test":[29,75],"image.":[30,76],"Then,":[31],"generated":[33],"recovered":[36],"over":[37,90],"bunch":[39],"training":[41,46],"images.":[47],"The":[48],"resulting":[49],"reconstruction":[50],"residuals":[51],"feed":[52],"mechanism":[55],"to":[56,73,86,105],"further":[57],"compose":[58],"final":[60,67],"residual":[61],"that":[62],"serves":[63],"for":[64],"inferring":[65],"decision":[68],"class":[71],"pertaining":[72],"Validated":[77],"on":[78],"benchmark":[80],"dataset,":[81],"presented":[83],"shows":[85],"promote":[87],"drastic":[88],"improvements":[89],"using":[91],"only":[92],"one":[93],"single":[94],"spectral":[95],"channel.":[96],"Furthermore,":[97],"encouraging":[98],"gains":[99],"have":[100],"been":[101],"recorded":[102],"with":[103],"respect":[104],"reference":[106],"works.":[107]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
