{"id":"https://openalex.org/W3131599653","doi":"https://doi.org/10.1109/igarss39084.2020.9323658","title":"Joint Spatial and Graph Convolutional Neural Networks - A Hybrid Model for Spatial-Spectral Geospatial Image Analysis","display_name":"Joint Spatial and Graph Convolutional Neural Networks - A Hybrid Model for Spatial-Spectral Geospatial Image Analysis","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3131599653","doi":"https://doi.org/10.1109/igarss39084.2020.9323658","mag":"3131599653"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323658","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323658","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","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":["University of Houston, USA"],"affiliations":[{"raw_affiliation_string":"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":["University of Houston, USA"],"affiliations":[{"raw_affiliation_string":"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":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.32432118,"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":"4003","last_page":"4006"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9961000084877014,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9663000106811523,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.863189160823822},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7440125942230225},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.722347617149353},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7048144936561584},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6561728119850159},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6446066498756409},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.541152834892273},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5242484211921692},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.48849260807037354},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45114749670028687},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41426557302474976},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32073384523391724},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.17256107926368713},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06643414497375488}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.863189160823822},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7440125942230225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.722347617149353},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7048144936561584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6561728119850159},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6446066498756409},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.541152834892273},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5242484211921692},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.48849260807037354},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45114749670028687},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41426557302474976},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32073384523391724},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.17256107926368713},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06643414497375488},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323658","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323658","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","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":14,"referenced_works":["https://openalex.org/W1503398984","https://openalex.org/W1521436688","https://openalex.org/W1522301498","https://openalex.org/W1966580635","https://openalex.org/W1991252559","https://openalex.org/W2194775991","https://openalex.org/W2765739551","https://openalex.org/W2768975974","https://openalex.org/W2908955282","https://openalex.org/W2920405132","https://openalex.org/W2963840672","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W6726873649"],"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/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W4401575149"],"abstract_inverted_index":{"How":[0],"to":[1,40],"efficiently":[2],"exploit":[3],"useful":[4],"information":[5,16],"from":[6,61],"hyperspectral":[7,46,62],"data":[8],"by":[9],"joint":[10],"analysis":[11],"of":[12,45,94],"spectral":[13],"and":[14,49,74,79],"spatial":[15,59],"is":[17],"an":[18],"important":[19],"problem.":[20],"In":[21],"this":[22,66],"work,":[23],"we":[24,69],"demonstrate":[25],"a":[26],"fusion":[27,67,73,77,83],"network":[28],"that":[29,81],"can":[30,56],"leverage":[31],"recent":[32],"developments":[33],"in":[34],"graph":[35],"convolutional":[36,51],"neural":[37,52],"networks":[38,53],"(GCNs)":[39],"effectively":[41],"analyze":[42],"reflectance":[43],"spectra":[44],"image":[47],"pixels":[48],"two-dimensional":[50],"(CNNs)":[54],"which":[55],"extract":[57],"object-specific":[58],"characteristics":[60],"image.":[63],"To":[64],"design":[65],"model,":[68],"study":[70],"both":[71],"decision":[72],"deep":[75,88],"feature":[76],"approaches":[78],"show":[80],"the":[82],"approach":[84],"outperforms":[85],"other":[86],"traditional":[87],"learning":[89],"methods":[90],"such":[91],"as":[92],"variants":[93],"CNNs.":[95]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}