{"id":"https://openalex.org/W2292221549","doi":"https://doi.org/10.1109/tgrs.2015.2484619","title":"Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images","display_name":"Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images","publication_year":2015,"publication_date":"2015-10-19","ids":{"openalex":"https://openalex.org/W2292221549","doi":"https://doi.org/10.1109/tgrs.2015.2484619","mag":"2292221549"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2015.2484619","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2015.2484619","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1708.05974","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"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":["Institute of Geographical Sciences, Remote Sensing and Geoinformatics, Freie Universit\u00e4t Berlin, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Geographical Sciences, Remote Sensing and Geoinformatics, Freie Universit\u00e4t Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I75951250"]}]},{"author_position":"last","author":{"id":null,"display_name":"Bjorn Waske","orcid":null},"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":["Institute of Geographical Sciences, Remote Sensing and Geoinformatics, Freie Universit\u00e4t Berlin, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Geographical Sciences, Remote Sensing and Geoinformatics, Freie Universit\u00e4t Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I75951250"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I75951250"],"apc_list":null,"apc_paid":null,"fwci":8.4186,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.97710124,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"54","issue":"3","first_page":"1623","last_page":"1634"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9812999963760376,"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":0.9812999963760376,"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":0.005100000184029341,"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":0.0017999999690800905,"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-imaging","display_name":"Hyperspectral imaging","score":0.842199981212616},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7520999908447266},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5630000233650208},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5565000176429749},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5245000123977661},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5152999758720398},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5081999897956848}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.842199981212616},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7520999908447266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7005000114440918},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6349999904632568},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5630000233650208},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5245000123977661},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5152999758720398},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5081999897956848},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5073999762535095},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.28949999809265137},{"id":"https://openalex.org/C114700698","wikidata":"https://www.wikidata.org/wiki/Q2882278","display_name":"Spectral bands","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2696000039577484},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26809999346733093}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2015.2484619","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2015.2484619","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1708.05974","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1708.05974","pdf_url":"https://arxiv.org/pdf/1708.05974","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1708.05974","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1708.05974","pdf_url":"https://arxiv.org/pdf/1708.05974","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1770500012","https://openalex.org/W1974774078","https://openalex.org/W1979158807","https://openalex.org/W1984792953","https://openalex.org/W1986464277","https://openalex.org/W1997565609","https://openalex.org/W1998030734","https://openalex.org/W2000650626","https://openalex.org/W2001298023","https://openalex.org/W2008847349","https://openalex.org/W2020282020","https://openalex.org/W2025803711","https://openalex.org/W2027878671","https://openalex.org/W2032488374","https://openalex.org/W2040399008","https://openalex.org/W2043755928","https://openalex.org/W2056621966","https://openalex.org/W2061443113","https://openalex.org/W2063907334","https://openalex.org/W2073936830","https://openalex.org/W2087263574","https://openalex.org/W2097915756","https://openalex.org/W2112589365","https://openalex.org/W2113464037","https://openalex.org/W2114819256","https://openalex.org/W2118246710","https://openalex.org/W2119897980","https://openalex.org/W2121110263","https://openalex.org/W2124571274","https://openalex.org/W2124889555","https://openalex.org/W2127199143","https://openalex.org/W2128659236","https://openalex.org/W2130835014","https://openalex.org/W2131864940","https://openalex.org/W2136251662","https://openalex.org/W2140499889","https://openalex.org/W2140996489","https://openalex.org/W2142940228","https://openalex.org/W2145035344","https://openalex.org/W2152414269","https://openalex.org/W2160547390","https://openalex.org/W2163112044","https://openalex.org/W2163398148","https://openalex.org/W2163886442","https://openalex.org/W2164330327","https://openalex.org/W2169500530","https://openalex.org/W2169535263","https://openalex.org/W4320339642","https://openalex.org/W6603586546","https://openalex.org/W6632282575","https://openalex.org/W6677725119"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,8,102],"sparse-representation-based":[4,141,163],"classification":[5,110],"approach":[6,32,134],"with":[7,152],"novel":[9],"dictionary":[10,87,97],"construction":[11],"procedure.":[12],"By":[13],"using":[14],"the":[15,22,26,36,93,96,106,116],"constructed":[16],"dictionary,":[17],"sophisticated":[18],"prior":[19],"knowledge":[20],"about":[21],"spatial":[23,47,147,159],"nature":[24],"of":[25,58,76,95,105,115],"image":[27,40,126],"can":[28,42],"be":[29,43],"integrated.":[30],"The":[31,108],"is":[33,60,69,81,111],"based":[34],"on":[35,122],"assumption":[37],"that":[38,131,143],"each":[39,89],"patch":[41],"factorized":[44],"into":[45],"characteristic":[46],"patterns,":[48],"also":[49],"called":[50],"shapelets,":[51],"and":[52,66,78,149,161],"patch-specific":[53],"spectral":[54,67,79],"information.":[55],"A":[56,74],"set":[57],"shapelets":[59,77],"learned":[61],"in":[62,83,138],"an":[63,84],"unsupervised":[64],"way,":[65],"information":[68,80,148,160],"embodied":[70],"by":[71,113],"training":[72],"samples.":[73],"combination":[75],"represented":[82],"undercomplete":[85],"spatial-spectral":[86],"for":[88],"individual":[90],"patch,":[91],"where":[92],"elements":[94],"are":[98,120],"linearly":[99],"combined":[100],"to":[101,140],"sparse":[103],"representation":[104,117],"patch.":[107],"patch-based":[109],"obtained":[112],"means":[114],"error.":[118],"Experiments":[119],"conducted":[121],"three":[123],"well-known":[124],"hyperspectral":[125],"data":[127],"sets.":[128],"They":[129],"illustrate":[130],"our":[132],"proposed":[133],"shows":[135],"superior":[136],"results":[137],"comparison":[139],"classifiers":[142,157],"use":[144],"only":[145],"limited":[146],"behaves":[150],"competitively":[151],"or":[153],"better":[154],"than":[155],"state-of-the-art":[156],"utilizing":[158],"kernelized":[162],"classifiers.":[164]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":11},{"year":2016,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2016-06-24T00:00:00"}