{"id":"https://openalex.org/W2436895464","doi":"https://doi.org/10.1109/chinacom.2015.7497979","title":"3D gray-gradient-gradient tensor field feature for hyperspectral image classification","display_name":"3D gray-gradient-gradient tensor field feature for hyperspectral image classification","publication_year":2015,"publication_date":"2015-08-01","ids":{"openalex":"https://openalex.org/W2436895464","doi":"https://doi.org/10.1109/chinacom.2015.7497979","mag":"2436895464"},"language":"en","primary_location":{"id":"doi:10.1109/chinacom.2015.7497979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/chinacom.2015.7497979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 10th International Conference on Communications and Networking in China (ChinaCom)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5083538110","display_name":"Zhaojun Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaojun Wu","raw_affiliation_strings":["Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100366955","display_name":"Qiang Wang","orcid":"https://orcid.org/0000-0002-2018-1764"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Wang","raw_affiliation_strings":["Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084291346","display_name":"Yi Shen","orcid":"https://orcid.org/0000-0002-0696-8253"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Shen","raw_affiliation_strings":["Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"23","issue":null,"first_page":"432","last_page":"436"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9951000213623047,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9907000064849854,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7945824265480042},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7801973819732666},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7794530391693115},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6235183477401733},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5933400392532349},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5722452402114868},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.4964454770088196},{"id":"https://openalex.org/keywords/structure-tensor","display_name":"Structure tensor","score":0.483866810798645},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4742804169654846},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.45497843623161316},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4362075626850128},{"id":"https://openalex.org/keywords/co-occurrence-matrix","display_name":"Co-occurrence matrix","score":0.4267711043357849},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4245496988296509},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37017762660980225},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.274860680103302},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2574087381362915}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7945824265480042},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7801973819732666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7794530391693115},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6235183477401733},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5933400392532349},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5722452402114868},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.4964454770088196},{"id":"https://openalex.org/C113315163","wikidata":"https://www.wikidata.org/wiki/Q7625159","display_name":"Structure tensor","level":3,"score":0.483866810798645},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4742804169654846},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.45497843623161316},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4362075626850128},{"id":"https://openalex.org/C117479156","wikidata":"https://www.wikidata.org/wiki/Q1543908","display_name":"Co-occurrence matrix","level":5,"score":0.4267711043357849},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4245496988296509},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37017762660980225},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.274860680103302},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2574087381362915},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/chinacom.2015.7497979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/chinacom.2015.7497979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 10th International Conference on Communications and Networking in China (ChinaCom)","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":20,"referenced_works":["https://openalex.org/W1601795611","https://openalex.org/W1663973292","https://openalex.org/W1977608287","https://openalex.org/W1995292093","https://openalex.org/W2003671357","https://openalex.org/W2024367395","https://openalex.org/W2024925667","https://openalex.org/W2036397044","https://openalex.org/W2043665634","https://openalex.org/W2044465660","https://openalex.org/W2056749682","https://openalex.org/W2105184687","https://openalex.org/W2131725398","https://openalex.org/W2132669662","https://openalex.org/W2150434735","https://openalex.org/W2156909104","https://openalex.org/W2159070926","https://openalex.org/W2365317855","https://openalex.org/W4230674625","https://openalex.org/W4320339642"],"related_works":["https://openalex.org/W2390142724","https://openalex.org/W2354234745","https://openalex.org/W2212283221","https://openalex.org/W2921950263","https://openalex.org/W1974700346","https://openalex.org/W2134401318","https://openalex.org/W2282697883","https://openalex.org/W1555506570","https://openalex.org/W2507620380","https://openalex.org/W1966354130"],"abstract_inverted_index":{"The":[0],"texture":[1,34,54,64],"feature":[2,100],"is":[3],"an":[4],"important":[5],"information":[6],"for":[7,102],"hyperspectral":[8,77],"image":[9],"classification.":[10],"In":[11],"this":[12],"study,":[13],"we":[14],"extend":[15],"the":[16,57,62,83,86],"traditional":[17,95],"2D":[18,96],"GLGCM(gray-level":[19],"gradient":[20,33],"cooccurrence":[21],"matrix)":[22],"into":[23,47],"3D":[24],"GGGTF(gray-gradient-gradient":[25],"tensor":[26],"field),":[27],"which":[28],"can":[29],"extract":[30],"gray":[31],"and":[32],"features":[35,44,65],"of":[36,56,85],"hyper-spectral":[37],"volume":[38],"data":[39],"simultaneously.":[40],"A":[41],"few":[42],"statistical":[43],"are":[45,66,79],"extended":[46],"third-order":[48],"forms":[49],"in":[50,99],"order":[51],"to":[52,81],"calculate":[53],"properties":[55],"generated":[58],"GGGTF.":[59,88],"And":[60],"then,":[61],"extracted":[63],"classified":[67],"by":[68],"linear":[69],"polynomial":[70],"kernel":[71],"SVM":[72],"classifier.":[73],"Two":[74],"widely":[75],"used":[76,80],"datasets":[78],"test":[82],"performance":[84],"proposed":[87],"Experimental":[89],"results":[90],"demonstrate":[91],"that":[92],"it":[93],"outperforms":[94],"GLGCM":[97],"method":[98],"extraction":[101],"supervised":[103],"classifications.":[104]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
