{"id":"https://openalex.org/W2793941577","doi":"https://doi.org/10.1109/icip.2017.8297014","title":"Multi-scale 3D deep convolutional neural network for hyperspectral image classification","display_name":"Multi-scale 3D deep convolutional neural network for hyperspectral image classification","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2793941577","doi":"https://doi.org/10.1109/icip.2017.8297014","mag":"2793941577"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8297014","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8297014","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5086729425","display_name":"Mingyi He","orcid":"https://orcid.org/0000-0003-2051-6955"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyi He","raw_affiliation_strings":["Northwestern Polytechnical University, International Center for Information Acquisition & Processing, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, International Center for Information Acquisition & Processing, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114375869","display_name":"Bo Li","orcid":"https://orcid.org/0009-0003-4088-1578"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Li","raw_affiliation_strings":["Northwestern Polytechnical University, International Center for Information Acquisition & Processing, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, International Center for Information Acquisition & Processing, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077493861","display_name":"Huahui Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huahui Chen","raw_affiliation_strings":["Northwestern Polytechnical University, International Center for Information Acquisition & Processing, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, International Center for Information Acquisition & Processing, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.353,"has_fulltext":false,"cited_by_count":412,"citation_normalized_percentile":{"value":0.98816207,"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":"3904","last_page":"3908"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.9939000010490417,"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"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9872000217437744,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7858452796936035},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.784988284111023},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7394676208496094},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7222805023193359},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7003122568130493},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6161410212516785},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5246094465255737},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5143052339553833},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5118586421012878},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.456022709608078},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4304535984992981},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3605618476867676},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.05507853627204895}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7858452796936035},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.784988284111023},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7394676208496094},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7222805023193359},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7003122568130493},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6161410212516785},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5246094465255737},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5143052339553833},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5118586421012878},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.456022709608078},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4304535984992981},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3605618476867676},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.05507853627204895},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8297014","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8297014","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1522734439","https://openalex.org/W1576462183","https://openalex.org/W1950365613","https://openalex.org/W1983364832","https://openalex.org/W2044439250","https://openalex.org/W2076063813","https://openalex.org/W2090424610","https://openalex.org/W2095705004","https://openalex.org/W2112796928","https://openalex.org/W2146502635","https://openalex.org/W2155893237","https://openalex.org/W2179290474","https://openalex.org/W2257669061","https://openalex.org/W2377273231","https://openalex.org/W2500751094","https://openalex.org/W2548340849","https://openalex.org/W2765725282","https://openalex.org/W6674330103","https://openalex.org/W6681435938"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W2952813363","https://openalex.org/W4378678253","https://openalex.org/W2911497689","https://openalex.org/W4360783045"],"abstract_inverted_index":{"Research":[0],"in":[1,80],"deep":[2,7,54],"neural":[3,56],"network":[4,57],"(DNN)":[5],"and":[6,17,28,73,118],"learning":[8],"has":[9],"great":[10],"progress":[11],"for":[12,61],"1D":[13,29,74],"(speech),":[14],"2D":[15,26,69],"(image)":[16],"3D":[18,36,53],"(3D-object)":[19],"recognition/classification":[20],"problems.":[21],"As":[22],"HSI":[23,62,78],"that":[24],"with":[25,89],"spatial":[27,71],"spectral":[30,75],"information":[31],"is":[32,59],"quite":[33],"different":[34],"from":[35,77],"object":[37],"image,":[38],"the":[39,106,110,115],"existing":[40],"DNN":[41],"cannot":[42],"be":[43],"directly":[44],"extended":[45],"to":[46,85],"hyperspectral":[47],"image":[48],"(HSI)":[49],"classification.":[50],"A":[51],"Multiscale":[52],"convolutional":[55],"(M3D-DCNN)":[58],"proposed":[60],"classification,":[63],"which":[64,113],"could":[65],"jointly":[66],"learn":[67],"both":[68],"Multi-scale":[70],"feature":[72,76],"data":[79],"an":[81],"end-to-end":[82],"approach,":[83],"promising":[84],"achieve":[86,105],"better":[87],"results":[88,108],"large-scale":[90],"dataset.":[91],"Although":[92],"without":[93],"any":[94],"hand-craft":[95],"features":[96],"or":[97],"pre/post-processing":[98],"like":[99],"PCA,":[100],"sparse":[101],"coding":[102],"etc,":[103],"we":[104],"state-of-the-art":[107],"on":[109],"standard":[111],"datasets,":[112],"shows":[114],"technical":[116],"validity":[117],"advancement":[119],"of":[120],"our":[121],"method.":[122]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":49},{"year":2024,"cited_by_count":71},{"year":2023,"cited_by_count":85},{"year":2022,"cited_by_count":70},{"year":2021,"cited_by_count":66},{"year":2020,"cited_by_count":34},{"year":2019,"cited_by_count":20},{"year":2018,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
