{"id":"https://openalex.org/W2887935602","doi":"https://doi.org/10.1109/civemsa.2018.8439982","title":"A Novel Reduced-Layer Deep Learning System via Pixel Rearrangement for Object Detection in Multispectral Imagery","display_name":"A Novel Reduced-Layer Deep Learning System via Pixel Rearrangement for Object Detection in Multispectral Imagery","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2887935602","doi":"https://doi.org/10.1109/civemsa.2018.8439982","mag":"2887935602"},"language":"en","primary_location":{"id":"doi:10.1109/civemsa.2018.8439982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa.2018.8439982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","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/A5040024264","display_name":"Anusha K. Vishwanathan","orcid":null},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anusha K. Vishwanathan","raw_affiliation_strings":["CMINDS Research Center, University of Massachusetts, Lowell, USA"],"affiliations":[{"raw_affiliation_string":"CMINDS Research Center, University of Massachusetts, Lowell, USA","institution_ids":["https://openalex.org/I133738476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001646142","display_name":"Dalila B. Megherbi","orcid":"https://orcid.org/0000-0002-1282-597X"},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dalila B. Megherbi","raw_affiliation_strings":["CMINDS Research Center, University of Massachusetts, Lowell, USA"],"affiliations":[{"raw_affiliation_string":"CMINDS Research Center, University of Massachusetts, Lowell, USA","institution_ids":["https://openalex.org/I133738476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5040024264"],"corresponding_institution_ids":["https://openalex.org/I133738476"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.12978253,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9968000054359436,"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":0.9968000054359436,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9962999820709229,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9961000084877014,"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/multispectral-image","display_name":"Multispectral image","score":0.8604254126548767},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7637084126472473},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7347015738487244},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6966615319252014},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5873755216598511},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5408445596694946},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5237027406692505},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5150253772735596},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4488258957862854},{"id":"https://openalex.org/keywords/multispectral-pattern-recognition","display_name":"Multispectral pattern recognition","score":0.43509641289711},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.05633381009101868}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.8604254126548767},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7637084126472473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7347015738487244},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6966615319252014},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5873755216598511},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5408445596694946},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5237027406692505},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5150253772735596},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4488258957862854},{"id":"https://openalex.org/C104541649","wikidata":"https://www.wikidata.org/wiki/Q6935090","display_name":"Multispectral pattern recognition","level":3,"score":0.43509641289711},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.05633381009101868},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/civemsa.2018.8439982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa.2018.8439982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","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/W639708223","https://openalex.org/W1522301498","https://openalex.org/W1665214252","https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1992570919","https://openalex.org/W1995903777","https://openalex.org/W1996119266","https://openalex.org/W2018948100","https://openalex.org/W2089181482","https://openalex.org/W2102605133","https://openalex.org/W2117539524","https://openalex.org/W2118572719","https://openalex.org/W2124571274","https://openalex.org/W2125899407","https://openalex.org/W2137063785","https://openalex.org/W2145587049","https://openalex.org/W2159411209","https://openalex.org/W2163605009","https://openalex.org/W2216125271","https://openalex.org/W2304648132","https://openalex.org/W2480078828","https://openalex.org/W2604762485","https://openalex.org/W2613718673","https://openalex.org/W2963542991","https://openalex.org/W2964121744","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6629368666","https://openalex.org/W6631190155","https://openalex.org/W6637242042","https://openalex.org/W6637373629","https://openalex.org/W6639824700","https://openalex.org/W6684191040","https://openalex.org/W6697974390","https://openalex.org/W6735951027","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2128126485","https://openalex.org/W4382563209","https://openalex.org/W2124952510","https://openalex.org/W2777937183","https://openalex.org/W2108633818","https://openalex.org/W1752760603","https://openalex.org/W1995889410","https://openalex.org/W4389779246","https://openalex.org/W1517037119","https://openalex.org/W2125510194"],"abstract_inverted_index":{"In":[0,43],"this":[1],"paper,":[2],"we":[3,45,190],"study":[4],"the":[5,65,69,82,104,112,121,126,144,153,160,165,184,202],"problem":[6],"of":[7,59,164,186],"object":[8],"detection":[9,119],"on":[10,52],"multispectral":[11,62],"images.":[12],"We":[13,110,124],"present":[14,192],"a":[15,26,48,57,73,193],"generalized":[16],"\u201cFully\u201d":[17],"Convolutional":[18],"Neural":[19],"Network":[20],"(FCNN)-based":[21],"deep":[22,139],"learning":[23,140],"system":[24,114],"with":[25,31,178,196],"novel":[27],"Pixel":[28],"Rearrangement":[29],"Technique,":[30],"reduced":[32,204],"computational":[33,205],"complexity":[34,206],"and":[35,98,137],"improved":[36],"prediction":[37,91,106],"accuracy":[38],"than":[39],"its":[40],"state-of-the-art":[41],"counterparts.":[42],"particular,":[44],"(a)":[46],"define":[47],"key":[49],"strategy":[50],"based":[51],"spectral":[53],"signatures":[54],"to":[55,108,142,159,200],"select":[56],"set":[58],"highly":[60],"informative":[61],"bands":[63],"for":[64,68,115],"system;":[66],"(b)":[67],"first":[70],"time,":[71],"introduce":[72],"pixel":[74],"rearrangement":[75],"technique":[76],"that":[77,86,102,171],"efficiently":[78],"utilizes":[79],"pixels":[80],"from":[81],"network's":[83],"feature":[84],"maps":[85],"results":[87],"into":[88],"accurate":[89],"pixelwise":[90,105],"images;":[92],"(c)":[93],"propose":[94],"dual":[95],"stage":[96],"global":[97],"adaptive":[99],"thresholding":[100],"methodologies":[101],"transform":[103],"images":[107],"binary.":[109],"evaluate":[111],"proposed":[113,145,203],"automatic":[116],"airborne":[117],"building":[118],"using":[120],"SpaceNet":[122,167],"dataset.":[123],"use":[125],"three":[127],"NVIDIA":[128],"GeForce":[129],"GTX":[130],"1060":[131],"GPUs":[132],"at":[133],"CMINDS":[134],"Research":[135],"Center":[136],"Tensorflow":[138],"framework":[141],"implement":[143],"system.":[146,207],"Our":[147],"findings":[148],"show":[149],"an":[150,179],"improvement":[151],"in":[152,157,174,183],"performance":[154],"by":[155],"0.3%":[156],"comparison":[158,194],"top":[161],"winning":[162],"submission":[163],"national":[166],"Building":[168],"Challenge":[169],"II,":[170],"took":[172],"place":[173],"April":[175],"2017,":[176],"but":[177],"additional":[180],"43%":[181],"reduction":[182],"number":[185],"FCNN":[187],"layers.":[188],"Finally,":[189],"also":[191],"chart":[195],"various":[197],"existing":[198],"approaches":[199],"highlight":[201]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
