{"id":"https://openalex.org/W2920405132","doi":"https://doi.org/10.1109/globalsip.2018.8645969","title":"GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR HYPERSPECTRAL DATA CLASSIFICATION","display_name":"GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR HYPERSPECTRAL DATA CLASSIFICATION","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2920405132","doi":"https://doi.org/10.1109/globalsip.2018.8645969","mag":"2920405132"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2018.8645969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2018.8645969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","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/A5090950917","display_name":"Farideh Foroozandeh Shahraki","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Farideh Foroozandeh Shahraki","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037823063","display_name":"Saurabh Prasad","orcid":"https://orcid.org/0000-0003-3729-9360"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saurabh Prasad","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston, USA","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090950917"],"corresponding_institution_ids":["https://openalex.org/I44461941"],"apc_list":null,"apc_paid":null,"fwci":4.1807,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.94689208,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"968","last_page":"972"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9929999709129333,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9786999821662903,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9299325346946716},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.6863928437232971},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6459325551986694},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6420297026634216},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5881542563438416},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5794261693954468},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.5540986657142639},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5346464514732361},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.5337207317352295},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4941881000995636},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4833797812461853},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.471749871969223},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4372982382774353},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.2734523415565491},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18751099705696106},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.12844246625900269}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9299325346946716},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.6863928437232971},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6459325551986694},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6420297026634216},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5881542563438416},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5794261693954468},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.5540986657142639},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5346464514732361},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.5337207317352295},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4941881000995636},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4833797812461853},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.471749871969223},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4372982382774353},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.2734523415565491},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18751099705696106},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.12844246625900269}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globalsip.2018.8645969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2018.8645969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W84941271","https://openalex.org/W637153065","https://openalex.org/W1522301498","https://openalex.org/W1578099820","https://openalex.org/W1662382123","https://openalex.org/W1999690352","https://openalex.org/W2063385051","https://openalex.org/W2097308346","https://openalex.org/W2109531142","https://openalex.org/W2111282613","https://openalex.org/W2154872931","https://openalex.org/W2158787690","https://openalex.org/W2194775991","https://openalex.org/W2276549099","https://openalex.org/W2612872092","https://openalex.org/W2742306779","https://openalex.org/W2756314412","https://openalex.org/W2768975974","https://openalex.org/W2950898568","https://openalex.org/W2962767366","https://openalex.org/W2964015378","https://openalex.org/W2964113829","https://openalex.org/W2964121744","https://openalex.org/W2964311892","https://openalex.org/W2964321699","https://openalex.org/W3014771378","https://openalex.org/W4294558607","https://openalex.org/W6620673361","https://openalex.org/W6631190155","https://openalex.org/W6637178625","https://openalex.org/W6676189307","https://openalex.org/W6682644385","https://openalex.org/W6685350579","https://openalex.org/W6690815549","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6737923298","https://openalex.org/W6738964360"],"related_works":["https://openalex.org/W4213150077","https://openalex.org/W2369410163","https://openalex.org/W2059018062","https://openalex.org/W2604585036","https://openalex.org/W2078477160","https://openalex.org/W1989103179","https://openalex.org/W1991172810","https://openalex.org/W125803343","https://openalex.org/W2153421018","https://openalex.org/W2117632582"],"abstract_inverted_index":{"Graph":[0,24],"based":[1,25],"manifold":[2,87],"learning":[3],"and":[4,22,99],"embedding":[5],"techniques":[6],"have":[7,30],"been":[8,31],"very":[9],"successful":[10],"at":[11],"representing":[12],"high":[13],"dimensional":[14],"hyperspectral":[15,71,111],"data":[16,61,91],"in":[17],"lower":[18],"dimensions":[19],"for":[20,34],"visualization":[21],"classification.":[23],"convolutional":[26],"neural":[27],"networks":[28],"(GCNs)":[29],"recently":[32],"developed":[33],"applications":[35],"on":[36,63],"high-dimensional":[37],"irregular":[38],"domains":[39],"represented":[40],"by":[41],"graphs,":[42],"such":[43,66],"as":[44,67],"citation":[45],"networks.":[46],"In":[47],"this":[48,123],"paper,":[49],"we":[50],"demonstrate":[51],"a":[52,77,93,100,109],"framework":[53],"that":[54,83,118],"can":[55],"leverage":[56],"GCNs":[57],"to":[58],"effectively":[59],"represent":[60],"residing":[62],"smooth":[64],"manifolds,":[65],"reflectance":[68],"spectra":[69,98],"of":[70,89,96,103,122],"image":[72,112],"pixels.":[73],"We":[74],"also":[75,116],"propose":[76],"robust":[78],"spatial-spectral":[79],"semi-supervised":[80],"adjacency":[81],"matrix":[82],"learns":[84],"the":[85,90,120],"underlying":[86],"structure":[88],"using":[92],"limited":[94],"amount":[95,102],"labeled":[97],"large":[101],"unlabeled":[104],"spectra.":[105],"Classification":[106],"performance":[107],"with":[108],"benchmark":[110],"analysis":[113],"dataset":[114],"is":[115],"provided":[117],"demonstrates":[119],"efficacy":[121],"approach.":[124]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":8}],"updated_date":"2026-05-10T08:33:47.465468","created_date":"2025-10-10T00:00:00"}