{"id":"https://openalex.org/W4402915461","doi":"https://doi.org/10.1109/icip51287.2024.10647482","title":"Semi-Supervised Graphical Deep Dictionary Learning for Hyperspectral Image Classification From Limited Samples","display_name":"Semi-Supervised Graphical Deep Dictionary Learning for Hyperspectral Image Classification From Limited Samples","publication_year":2024,"publication_date":"2024-09-27","ids":{"openalex":"https://openalex.org/W4402915461","doi":"https://doi.org/10.1109/icip51287.2024.10647482"},"language":"en","primary_location":{"id":"doi:10.1109/icip51287.2024.10647482","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip51287.2024.10647482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","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/A5004746534","display_name":"Anurag Goel","orcid":"https://orcid.org/0000-0003-2660-7540"},"institutions":[{"id":"https://openalex.org/I863896202","display_name":"Delhi Technological University","ror":"https://ror.org/01ztcvt22","country_code":"IN","type":"education","lineage":["https://openalex.org/I863896202"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anurag Goel","raw_affiliation_strings":["Delhi Technological University,New Delhi,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Delhi Technological University,New Delhi,India","institution_ids":["https://openalex.org/I863896202"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020310463","display_name":"Angshul Majumdar","orcid":"https://orcid.org/0000-0002-1065-3000"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Angshul Majumdar","raw_affiliation_strings":["IAI, TCG CREST,Kolkata,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IAI, TCG CREST,Kolkata,India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.287,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.61465702,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2108","last_page":"2114"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.8841000199317932,"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":0.8841000199317932,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.7645999789237976,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8608688116073608},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.747717559337616},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7406454086303711},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6463267803192139},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.6286836862564087},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5169321894645691},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.33906659483909607}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8608688116073608},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.747717559337616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7406454086303711},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6463267803192139},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.6286836862564087},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5169321894645691},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33906659483909607}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip51287.2024.10647482","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip51287.2024.10647482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1991359796","https://openalex.org/W2002932002","https://openalex.org/W2029316659","https://openalex.org/W2047705660","https://openalex.org/W2141039087","https://openalex.org/W2160660350","https://openalex.org/W2398395170","https://openalex.org/W2517949381","https://openalex.org/W2560042709","https://openalex.org/W2625436554","https://openalex.org/W2770315464","https://openalex.org/W2890271586","https://openalex.org/W2898381489","https://openalex.org/W2901819993","https://openalex.org/W2908159312","https://openalex.org/W2963146412","https://openalex.org/W2964383635","https://openalex.org/W2983785920","https://openalex.org/W3000978536","https://openalex.org/W3024865103","https://openalex.org/W3122028341","https://openalex.org/W3133704440","https://openalex.org/W3201357114","https://openalex.org/W4210541032","https://openalex.org/W4212774754","https://openalex.org/W4220853886","https://openalex.org/W4226070402","https://openalex.org/W4367320924"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2291489469","https://openalex.org/W2546503577"],"abstract_inverted_index":{"In":[0],"this":[1,52,79],"work,":[2],"we":[3],"propose":[4],"a":[5,55,60,82],"semi-supervised":[6,56],"deep":[7,21],"feature":[8],"generation":[9],"network":[10],"that":[11,39,101],"accounts":[12,28],"for":[13,29,54],"local":[14],"similarities.":[15],"It":[16],"is":[17,50],"based":[18],"on":[19,94],"the":[20,37,40,72,98,106],"dictionary":[22],"learning":[23],"(DDL)":[24],"framework.":[25],"The":[26],"formulation":[27,57,87],"two":[30,95],"unique":[31],"aspects":[32],"of":[33,43],"hyperspectral":[34],"classification.":[35],"First,":[36],"fact":[38],"total":[41],"number":[42],"pixels":[44,62,75],"/":[45,63,74],"samples":[46,64,73],"to":[47,65,81],"be":[48,66],"labeled":[49,67],"constant;":[51],"allows":[53],"allowing":[58],"only":[59],"few":[61],"as":[68],"training":[69],"data.":[70],"Second,":[71],"are":[76],"spatially":[77],"correlated;":[78],"leads":[80],"graph":[83],"regularization":[84],"formulation.":[85],"Our":[86],"has":[88],"been":[89],"benchmarked":[90],"with":[91],"state-of-the-art":[92],"techniques":[93],"popular":[96],"datasets;":[97],"results":[99],"show":[100],"our":[102],"work":[103],"improves":[104],"upon":[105],"ones":[107],"compared":[108],"against.":[109]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
