{"id":"https://openalex.org/W2020956428","doi":"https://doi.org/10.1109/igarss.2015.7326282","title":"Landcover classification with self-taught learning on archetypal dictionaries","display_name":"Landcover classification with self-taught learning on archetypal dictionaries","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W2020956428","doi":"https://doi.org/10.1109/igarss.2015.7326282","mag":"2020956428"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2015.7326282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2015.7326282","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/A5043510754","display_name":"Ribana Roscher","orcid":"https://orcid.org/0000-0003-0094-6210"},"institutions":[{"id":"https://openalex.org/I75951250","display_name":"Freie Universit\u00e4t Berlin","ror":"https://ror.org/046ak2485","country_code":"DE","type":"education","lineage":["https://openalex.org/I75951250"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Ribana Roscher","raw_affiliation_strings":["Division of Remote Sensing and Geoinformatics, Institute of Geographical Sciences Freie Universitat Berlin, Berlin","Division of Remote Sensing and Geoinformatics, Institute of Geographical Sciences Freie Universit\u00e4t Berlin, Malteserstr. 74-100, 12249 Berlin"],"affiliations":[{"raw_affiliation_string":"Division of Remote Sensing and Geoinformatics, Institute of Geographical Sciences Freie Universitat Berlin, Berlin","institution_ids":["https://openalex.org/I75951250"]},{"raw_affiliation_string":"Division of Remote Sensing and Geoinformatics, Institute of Geographical Sciences Freie Universit\u00e4t Berlin, Malteserstr. 74-100, 12249 Berlin","institution_ids":["https://openalex.org/I75951250"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016451777","display_name":"Christoph R\u00f6mer","orcid":null},"institutions":[{"id":"https://openalex.org/I135140700","display_name":"University of Bonn","ror":"https://ror.org/041nas322","country_code":"DE","type":"education","lineage":["https://openalex.org/I135140700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christoph Romer","raw_affiliation_strings":["Department of Geoinformation, University of Bonn, Bonn","Department of Geoinformation, Institute of Geodesy and Geoinformation, University of Bonn, Meckenheimer Allee 172, 53115 Bonn"],"affiliations":[{"raw_affiliation_string":"Department of Geoinformation, University of Bonn, Bonn","institution_ids":["https://openalex.org/I135140700"]},{"raw_affiliation_string":"Department of Geoinformation, Institute of Geodesy and Geoinformation, University of Bonn, Meckenheimer Allee 172, 53115 Bonn","institution_ids":["https://openalex.org/I135140700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018040226","display_name":"Bj\u00f6rn Waske","orcid":"https://orcid.org/0000-0002-2586-3748"},"institutions":[{"id":"https://openalex.org/I75951250","display_name":"Freie Universit\u00e4t Berlin","ror":"https://ror.org/046ak2485","country_code":"DE","type":"education","lineage":["https://openalex.org/I75951250"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bjorn Waske","raw_affiliation_strings":["Division of Remote Sensing and Geoinformatics, Institute of Geographical Sciences Freie Universitat Berlin, Berlin","Division of Remote Sensing and Geoinformatics, Institute of Geographical Sciences Freie Universit\u00e4t Berlin, Malteserstr. 74-100, 12249 Berlin"],"affiliations":[{"raw_affiliation_string":"Division of Remote Sensing and Geoinformatics, Institute of Geographical Sciences Freie Universitat Berlin, Berlin","institution_ids":["https://openalex.org/I75951250"]},{"raw_affiliation_string":"Division of Remote Sensing and Geoinformatics, Institute of Geographical Sciences Freie Universit\u00e4t Berlin, Malteserstr. 74-100, 12249 Berlin","institution_ids":["https://openalex.org/I75951250"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076668701","display_name":"Lutz Pl\u00fcmer","orcid":null},"institutions":[{"id":"https://openalex.org/I135140700","display_name":"University of Bonn","ror":"https://ror.org/041nas322","country_code":"DE","type":"education","lineage":["https://openalex.org/I135140700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lutz Plumer","raw_affiliation_strings":["Department of Geoinformation, University of Bonn, Bonn","Department of Geoinformation, Institute of Geodesy and Geoinformation, University of Bonn, Meckenheimer Allee 172, 53115 Bonn"],"affiliations":[{"raw_affiliation_string":"Department of Geoinformation, University of Bonn, Bonn","institution_ids":["https://openalex.org/I135140700"]},{"raw_affiliation_string":"Department of Geoinformation, Institute of Geodesy and Geoinformation, University of Bonn, Meckenheimer Allee 172, 53115 Bonn","institution_ids":["https://openalex.org/I135140700"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5043510754"],"corresponding_institution_ids":["https://openalex.org/I75951250"],"apc_list":null,"apc_paid":null,"fwci":0.8441,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.77842738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9995999932289124,"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":0.9995999932289124,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9588000178337097,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7211751341819763},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7138254642486572},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7046680450439453},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6076870560646057},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5823873281478882},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.46394702792167664},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45082801580429077},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.44923266768455505},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4484635591506958},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4174521565437317},{"id":"https://openalex.org/keywords/linear-classifier","display_name":"Linear classifier","score":0.4102674722671509},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1665763258934021},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1512381136417389}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7211751341819763},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7138254642486572},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7046680450439453},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6076870560646057},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5823873281478882},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.46394702792167664},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45082801580429077},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.44923266768455505},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4484635591506958},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4174521565437317},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.4102674722671509},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1665763258934021},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1512381136417389},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2015.7326282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2015.7326282","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"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320319877","display_name":"Fields Institute for Research in Mathematical Sciences","ror":"https://ror.org/03zzj3f20"},{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2017805562","https://openalex.org/W2103504431","https://openalex.org/W2106706131","https://openalex.org/W2115549875","https://openalex.org/W2122922389","https://openalex.org/W2128659236","https://openalex.org/W2142012908","https://openalex.org/W2160547390","https://openalex.org/W4253636695","https://openalex.org/W6677446445"],"related_works":["https://openalex.org/W3208297503","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353","https://openalex.org/W2129933262","https://openalex.org/W2599254681","https://openalex.org/W2898108274","https://openalex.org/W2162970382"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"archetypal":[3,72],"dictionaries":[4,73],"for":[5,10],"a":[6,38,45,52,83],"self-taught":[7,69],"learning":[8,21,70],"framework":[9],"the":[11,56,98],"application":[12],"of":[13,51,58],"landcover":[14],"classification.":[15],"Self-taught":[16],"learning,":[17],"an":[18],"unsupervised":[19],"representation":[20],"method,":[22],"is":[23],"exploited":[24],"to":[25,94],"learn":[26],"low-dimensional":[27],"and":[28],"discriminative":[29],"higher-level":[30],"features,":[31,75],"which":[32,76],"are":[33,42,92],"used":[34,79],"as":[35,80],"input":[36,81],"into":[37,82],"classification":[39,90],"algorithm.":[40],"Experiments":[41],"conducted":[43],"using":[44,97],"multi-spectral":[46],"Landsat":[47],"5":[48],"TM":[49],"image":[50],"study":[53],"area":[54],"in":[55,62],"north":[57],"Novo":[59],"Progresso":[60],"located":[61],"South":[63],"America.":[64],"Our":[65],"results":[66],"confirm":[67],"that":[68],"with":[71],"provide":[74],"can":[77],"be":[78],"linear":[84],"logistic":[85],"regression":[86],"classifier.":[87],"The":[88],"obtained":[89],"accuracies":[91],"comparable":[93],"kernel-based":[95],"classifier":[96],"original":[99],"features.":[100]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}