{"id":"https://openalex.org/W2628046464","doi":"https://doi.org/10.1109/jstars.2017.2712632","title":"Biased Run-Length Coding of Bilevel Classification Label Maps of Hyperspectral Images","display_name":"Biased Run-Length Coding of Bilevel Classification Label Maps of Hyperspectral Images","publication_year":2017,"publication_date":"2017-06-22","ids":{"openalex":"https://openalex.org/W2628046464","doi":"https://doi.org/10.1109/jstars.2017.2712632","mag":"2628046464"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2017.2712632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2017.2712632","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5041565692","display_name":"Amir Liaghati","orcid":"https://orcid.org/0000-0002-2708-3067"},"institutions":[{"id":"https://openalex.org/I1295339012","display_name":"Boeing (United States)","ror":"https://ror.org/04sm5zn07","country_code":"US","type":"company","lineage":["https://openalex.org/I1295339012"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Amir Leon Liaghati","raw_affiliation_strings":["Boeing Company, Huntsville, AL, USA"],"affiliations":[{"raw_affiliation_string":"Boeing Company, Huntsville, AL, USA","institution_ids":["https://openalex.org/I1295339012"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015897741","display_name":"W. David Pan","orcid":"https://orcid.org/0000-0001-7265-2188"},"institutions":[{"id":"https://openalex.org/I82495205","display_name":"University of Alabama in Huntsville","ror":"https://ror.org/02zsxwr40","country_code":"US","type":"education","lineage":["https://openalex.org/I82495205"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"W. David Pan","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL, USA","institution_ids":["https://openalex.org/I82495205"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102510891","display_name":"Zhuocheng Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I82495205","display_name":"University of Alabama in Huntsville","ror":"https://ror.org/02zsxwr40","country_code":"US","type":"education","lineage":["https://openalex.org/I82495205"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuocheng Jiang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL, USA","institution_ids":["https://openalex.org/I82495205"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5041565692"],"corresponding_institution_ids":["https://openalex.org/I1295339012"],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":null,"fwci":0.7063,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75619629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"10","issue":"10","first_page":"4580","last_page":"4588"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994000196456909,"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":0.9994000196456909,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.998199999332428,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9965999722480774,"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/huffman-coding","display_name":"Huffman coding","score":0.916449785232544},{"id":"https://openalex.org/keywords/tunstall-coding","display_name":"Tunstall coding","score":0.7189475297927856},{"id":"https://openalex.org/keywords/arithmetic-coding","display_name":"Arithmetic coding","score":0.6340858340263367},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6293367147445679},{"id":"https://openalex.org/keywords/shannon\u2013fano-coding","display_name":"Shannon\u2013Fano coding","score":0.5816496014595032},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5637539625167847},{"id":"https://openalex.org/keywords/variable-length-code","display_name":"Variable-length code","score":0.5565252900123596},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5524470210075378},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5377021431922913},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5355308651924133},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5151323080062866},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.491569846868515},{"id":"https://openalex.org/keywords/entropy-encoding","display_name":"Entropy encoding","score":0.44448941946029663},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4217260181903839},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.41892069578170776},{"id":"https://openalex.org/keywords/context-adaptive-binary-arithmetic-coding","display_name":"Context-adaptive binary arithmetic coding","score":0.4121396243572235},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.23825877904891968},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16713577508926392}],"concepts":[{"id":"https://openalex.org/C46900642","wikidata":"https://www.wikidata.org/wiki/Q2647","display_name":"Huffman coding","level":3,"score":0.916449785232544},{"id":"https://openalex.org/C73231260","wikidata":"https://www.wikidata.org/wiki/Q7853376","display_name":"Tunstall coding","level":4,"score":0.7189475297927856},{"id":"https://openalex.org/C153338461","wikidata":"https://www.wikidata.org/wiki/Q2651","display_name":"Arithmetic coding","level":4,"score":0.6340858340263367},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6293367147445679},{"id":"https://openalex.org/C130811719","wikidata":"https://www.wikidata.org/wiki/Q2645","display_name":"Shannon\u2013Fano coding","level":4,"score":0.5816496014595032},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5637539625167847},{"id":"https://openalex.org/C60603091","wikidata":"https://www.wikidata.org/wiki/Q2981616","display_name":"Variable-length code","level":3,"score":0.5565252900123596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5524470210075378},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5377021431922913},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5355308651924133},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5151323080062866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.491569846868515},{"id":"https://openalex.org/C1769480","wikidata":"https://www.wikidata.org/wiki/Q1345239","display_name":"Entropy encoding","level":3,"score":0.44448941946029663},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4217260181903839},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.41892069578170776},{"id":"https://openalex.org/C175732694","wikidata":"https://www.wikidata.org/wiki/Q1128713","display_name":"Context-adaptive binary arithmetic coding","level":3,"score":0.4121396243572235},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.23825877904891968},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16713577508926392}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstars.2017.2712632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2017.2712632","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W616767364","https://openalex.org/W1497909013","https://openalex.org/W1977917189","https://openalex.org/W1985961973","https://openalex.org/W1987572442","https://openalex.org/W2021659659","https://openalex.org/W2029256086","https://openalex.org/W2038085095","https://openalex.org/W2065684331","https://openalex.org/W2067610958","https://openalex.org/W2075366655","https://openalex.org/W2076292074","https://openalex.org/W2088780140","https://openalex.org/W2090352468","https://openalex.org/W2112446952","https://openalex.org/W2116949556","https://openalex.org/W2123810473","https://openalex.org/W2125074870","https://openalex.org/W2131395393","https://openalex.org/W2136251662","https://openalex.org/W2139149233","https://openalex.org/W2151665911","https://openalex.org/W2157700496","https://openalex.org/W2165564574","https://openalex.org/W2185198032","https://openalex.org/W2497197609","https://openalex.org/W2523001693","https://openalex.org/W2535713326","https://openalex.org/W2539715321","https://openalex.org/W2565676669","https://openalex.org/W2574481137","https://openalex.org/W2981627284","https://openalex.org/W3099916806","https://openalex.org/W4285719527","https://openalex.org/W6630738462","https://openalex.org/W6672569647","https://openalex.org/W6686259835"],"related_works":["https://openalex.org/W2156106714","https://openalex.org/W4312628177","https://openalex.org/W1579872668","https://openalex.org/W1587903609","https://openalex.org/W2123072960","https://openalex.org/W2479819259","https://openalex.org/W2668751350","https://openalex.org/W1994532706","https://openalex.org/W2213686625","https://openalex.org/W1499106570"],"abstract_inverted_index":{"For":[0],"efficient":[1],"coding":[2,26,46,104,125,139,167,189],"of":[3,16,55,66,69,110,147,152,155,195],"bilevel":[4],"sources":[5],"with":[6],"some":[7],"dominant":[8],"symbols":[9,33,112],"often":[10],"found":[11],"in":[12,42],"classification":[13,192],"label":[14,193],"maps":[15,194],"hyperspectral":[17],"images,":[18],"we":[19,51],"proposed":[20],"a":[21,115],"novel":[22],"biased":[23],"run-length":[24,138,188],"(BRL)":[25],"method,":[27],"which":[28,43,71],"codes":[29],"the":[30,40,44,56,64,83,93,102,108,123,136,153,156,175,186,196],"most":[31],"probable":[32],"separately":[34],"from":[35],"other":[36],"symbols.":[37],"To":[38],"determine":[39],"conditions":[41],"BRL":[45,103,124],"method":[47,57,105,126,178,190],"would":[48],"be":[49],"effective,":[50],"conducted":[52],"an":[53],"analysis":[54,80,120],"using":[58],"statistical":[59],"models.":[60],"We":[61,99],"first":[62],"analyzed":[63,101],"effect":[65],"2-D":[67],"blocking":[68],"pixels,":[70],"were":[72],"assumed":[73],"to":[74,88,128,170,181],"have":[75,89,132],"generalized":[76],"Gaussian":[77],"distributions.":[78],"The":[79],"showed":[81,121,150],"that":[82,122,131],"resulting":[84],"symbol":[85,97,162],"blocks":[86],"tended":[87,127],"lower":[90,133],"entropies":[91,134],"than":[92,135,174,185],"original":[94],"source":[95],"without":[96],"blocking.":[98],"then":[100],"applied":[106],"on":[107,144,191],"sequence":[109],"block":[111],"characterized":[113],"by":[114],"first-order":[116],"Markov":[117],"model.":[118],"Information-theoretic":[119],"generate":[129],"codewords":[130],"conventional":[137,187],"method.":[140],"Furthermore,":[141],"numerical":[142],"simulations":[143],"lossless":[145],"compression":[146,173,184],"actual":[148],"data":[149],"improvement":[151],"state":[154],"art.":[157],"Specifically,":[158],"end-to-end":[159],"implementation":[160],"integrating":[161],"blocking,":[163],"BRL,":[164],"and":[165,179],"Huffman":[166],"achieved":[168],"up":[169,180],"4.3%":[171],"higher":[172,183],"JBIG2":[176],"standard":[177],"3.2%":[182],"widely":[197],"used":[198],"\u201cIndian":[199],"Pines\u201d":[200],"dataset.":[201]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}