{"id":"https://openalex.org/W2986595877","doi":"https://doi.org/10.1109/igarss.2019.8899049","title":"Unsupervised Discriminative Dimension Reduction for Hyperspectral Chemical Plume Segmentation","display_name":"Unsupervised Discriminative Dimension Reduction for Hyperspectral Chemical Plume Segmentation","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2986595877","doi":"https://doi.org/10.1109/igarss.2019.8899049","mag":"2986595877"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8899049","is_oa":false,"landing_page_url":"http://doi.org/10.1109/igarss.2019.8899049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 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/A5022923838","display_name":"James M. Murphy","orcid":"https://orcid.org/0000-0001-6598-044X"},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"James M. Murphy","raw_affiliation_strings":["Tufts University,Department of Mathematics,,Medford,MA,USA,02155"],"affiliations":[{"raw_affiliation_string":"Tufts University,Department of Mathematics,,Medford,MA,USA,02155","institution_ids":["https://openalex.org/I121934306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089371996","display_name":"Mauro Maggioni","orcid":"https://orcid.org/0000-0003-3258-9297"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mauro Maggioni","raw_affiliation_strings":["Johns Hopkins University,Department of Mathematics,,Baltimore,MD,USA,21218"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University,Department of Mathematics,,Baltimore,MD,USA,21218","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5022923838"],"corresponding_institution_ids":["https://openalex.org/I121934306"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.19328216,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"14","issue":null,"first_page":"3828","last_page":"3831"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9997000098228455,"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":0.9997000098228455,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7817599773406982},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.74347984790802},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6873295903205872},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.6384606957435608},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6259384155273438},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5983557105064392},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5765484571456909},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5365732908248901},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5329901576042175},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4343263506889343},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.42439407110214233},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34198200702667236},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.327176034450531},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11955463886260986}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7817599773406982},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.74347984790802},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6873295903205872},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6384606957435608},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6259384155273438},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5983557105064392},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5765484571456909},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5365732908248901},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5329901576042175},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4343263506889343},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.42439407110214233},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34198200702667236},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.327176034450531},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11955463886260986},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8899049","is_oa":false,"landing_page_url":"http://doi.org/10.1109/igarss.2019.8899049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7900000214576721,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1531259569","https://openalex.org/W1976251851","https://openalex.org/W1993962865","https://openalex.org/W2006554089","https://openalex.org/W2029316659","https://openalex.org/W2095088894","https://openalex.org/W2123649031","https://openalex.org/W2126447858","https://openalex.org/W2136251662","https://openalex.org/W2140140790","https://openalex.org/W2165835468","https://openalex.org/W2165874743","https://openalex.org/W2799770756","https://openalex.org/W2895913707","https://openalex.org/W2912209512","https://openalex.org/W2953960221","https://openalex.org/W2963558844","https://openalex.org/W2963735455","https://openalex.org/W2990418500","https://openalex.org/W3099395583","https://openalex.org/W4213367101","https://openalex.org/W6632006871","https://openalex.org/W6684578312","https://openalex.org/W6755172055"],"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/W2292979300","https://openalex.org/W2120164251","https://openalex.org/W4390806283"],"abstract_inverted_index":{"We":[0,65],"propose":[1],"a":[2,48],"novel":[3],"algorithm":[4],"for":[5,69],"unsupervised":[6,70,84],"segmentation":[7,73],"of":[8,21],"hyperspectral":[9],"imagery":[10],"(HSI).":[11],"Representative":[12],"cluster":[13],"modes":[14,56],"are":[15],"learned":[16,55],"through":[17],"the":[18,22,45,54,62],"diffusion":[19],"geometry":[20],"HSI,":[23,75],"which":[24],"is":[25,36],"highly":[26],"invariant":[27],"to":[28,43,58],"non-linearities":[29],"present":[30],"in":[31,61,74],"HSI":[32],"clusters.":[33],"Mode":[34],"detection":[35],"followed":[37],"by":[38],"partial":[39],"least":[40],"squares":[41],"regression":[42],"project":[44],"data":[46],"onto":[47],"low-dimensional":[49,63],"space":[50],"that":[51],"discriminates":[52],"between":[53],"and":[57,82],"assign":[59],"labels":[60],"space.":[64],"evaluate":[66],"this":[67],"method":[68],"chemical":[71],"plume":[72],"showing":[76],"it":[77],"performs":[78],"competitively":[79],"versus":[80],"benchmark":[81],"state-of-the-art":[83],"learning":[85],"techniques.":[86]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}