{"id":"https://openalex.org/W4402674181","doi":"https://doi.org/10.1109/tgrs.2024.3465043","title":"Onboard Deep Lossless and Near-Lossless Predictive Coding of Hyperspectral Images With Line-Based Attention","display_name":"Onboard Deep Lossless and Near-Lossless Predictive Coding of Hyperspectral Images With Line-Based Attention","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4402674181","doi":"https://doi.org/10.1109/tgrs.2024.3465043"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3465043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3465043","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5058182410","display_name":"Diego Valsesia","orcid":"https://orcid.org/0000-0003-1997-2910"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Politecnico di Torino","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Diego Valsesia","raw_affiliation_strings":["Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy","Department of Electronics and Telecommunications, Politecnico di Torino, Italy"],"raw_orcid":"https://orcid.org/0000-0003-1997-2910","affiliations":[{"raw_affiliation_string":"Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy","institution_ids":["https://openalex.org/I177477856"]},{"raw_affiliation_string":"Department of Electronics and Telecommunications, Politecnico di Torino, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054395599","display_name":"Tiziano Bianchi","orcid":"https://orcid.org/0000-0002-3965-3522"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Politecnico di Torino","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Tiziano Bianchi","raw_affiliation_strings":["Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy","Department of Electronics and Telecommunications, Politecnico di Torino, Italy"],"raw_orcid":"https://orcid.org/0000-0002-3965-3522","affiliations":[{"raw_affiliation_string":"Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy","institution_ids":["https://openalex.org/I177477856"]},{"raw_affiliation_string":"Department of Electronics and Telecommunications, Politecnico di Torino, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034486931","display_name":"Enrico Magli","orcid":"https://orcid.org/0000-0002-0901-0251"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Politecnico di Torino","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Enrico Magli","raw_affiliation_strings":["Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy","Department of Electronics and Telecommunications, Politecnico di Torino, Italy"],"raw_orcid":"https://orcid.org/0000-0002-0901-0251","affiliations":[{"raw_affiliation_string":"Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy","institution_ids":["https://openalex.org/I177477856"]},{"raw_affiliation_string":"Department of Electronics and Telecommunications, Politecnico di Torino, Italy","institution_ids":["https://openalex.org/I177477856"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I177477856"],"apc_list":null,"apc_paid":null,"fwci":2.7116,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.91121091,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9961000084877014,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9961000084877014,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9940000176429749,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9898999929428101,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8134123086929321},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.7714818716049194},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6448379755020142},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6138778328895569},{"id":"https://openalex.org/keywords/predictive-coding","display_name":"Predictive coding","score":0.5825425386428833},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4212369918823242},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.42062073945999146},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.41713374853134155},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.3872838318347931},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36028480529785156},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.18420535326004028},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11388283967971802}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8134123086929321},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.7714818716049194},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6448379755020142},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6138778328895569},{"id":"https://openalex.org/C2778061373","wikidata":"https://www.wikidata.org/wiki/Q1315146","display_name":"Predictive coding","level":3,"score":0.5825425386428833},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4212369918823242},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.42062073945999146},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.41713374853134155},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3872838318347931},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36028480529785156},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.18420535326004028},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11388283967971802},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2024.3465043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3465043","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W2010319424","https://openalex.org/W2064675550","https://openalex.org/W2329053551","https://openalex.org/W2416989340","https://openalex.org/W2423557781","https://openalex.org/W2552465432","https://openalex.org/W2785562966","https://openalex.org/W2956104838","https://openalex.org/W2965631471","https://openalex.org/W2978517431","https://openalex.org/W3090326207","https://openalex.org/W3095497211","https://openalex.org/W3127964924","https://openalex.org/W3141915823","https://openalex.org/W3150201565","https://openalex.org/W3164997792","https://openalex.org/W3173321568","https://openalex.org/W3176350758","https://openalex.org/W4281257095","https://openalex.org/W4296339430","https://openalex.org/W4309134821","https://openalex.org/W4313306370","https://openalex.org/W4319069095","https://openalex.org/W4368232741","https://openalex.org/W4379116708","https://openalex.org/W4385245566","https://openalex.org/W4386547159","https://openalex.org/W4387829368","https://openalex.org/W4387986484","https://openalex.org/W4389524555","https://openalex.org/W4390480870","https://openalex.org/W4391467940","https://openalex.org/W4393207062","https://openalex.org/W4402264941","https://openalex.org/W6694251005","https://openalex.org/W6745265922","https://openalex.org/W6754669699","https://openalex.org/W6859298233"],"related_works":["https://openalex.org/W2279964071","https://openalex.org/W2948148442","https://openalex.org/W2461250372","https://openalex.org/W2394342941","https://openalex.org/W2169853506","https://openalex.org/W2547124190","https://openalex.org/W2350586049","https://openalex.org/W2385628723","https://openalex.org/W2057878850","https://openalex.org/W2169871401"],"abstract_inverted_index":{"Deep":[0],"learning":[1,137,144],"methods":[2],"have":[3],"traditionally":[4],"been":[5],"difficult":[6],"to":[7,9,17,23,70,76,146],"apply":[8],"compression":[10,106],"of":[11,33,91,100,111,120],"hyperspectral":[12],"images":[13],"onboard":[14],"spacecrafts":[15],"due":[16],"the":[18,31,46,88,94,109,121,141],"large":[19],"computational":[20],"complexity":[21,96],"needed":[22],"achieve":[24,77],"adequate":[25],"representational":[26,89],"power,":[27],"as":[28,30],"well":[29],"lack":[32],"suitable":[34],"datasets":[35,126],"for":[36],"training":[37],"and":[38,50,97,139,151],"testing.":[39],"In":[40,74],"this":[41],"article,":[42],"we":[43,51,79],"depart":[44],"from":[45],"traditional":[47],"autoencoder":[48],"approach,":[49],"design":[52],"a":[53,81,161],"predictive":[54],"neural":[55,102],"network,":[56],"called":[57],"line":[58,67,69],"receptance":[59],"weighted":[60],"key":[61],"value":[62],"(LineRWKV),":[63],"which":[64],"works":[65],"recursively":[66],"by":[68,117],"limit":[71],"memory":[72],"consumption.":[73],"order":[75],"that,":[78],"adopt":[80],"novel":[82],"hybrid":[83],"attentive-recursive":[84],"operation":[85],"that":[86,128],"combines":[87],"advantages":[90],"Transformers":[92],"with":[93],"linear":[95],"recursive":[98],"implementation":[99],"recurrent":[101],"networks":[103],"(RNNs).":[104],"The":[105],"algorithm":[107],"performs":[108],"prediction":[110],"each":[112],"pixel":[113],"using":[114],"LineRWKV,":[115],"followed":[116],"entropy":[118],"coding":[119],"residual.":[122],"Experiments":[123],"on":[124,160],"multiple":[125],"show":[127],"LineRWKV":[129],"is":[130,140],"highly":[131],"memory-efficient,":[132],"significantly":[133],"outperforms":[134],"state-of-the-art":[135],"deep":[136,143],"methods,":[138],"first":[142],"approach":[145],"outperform":[147],"CCSDS-123.0-B-2":[148],"at":[149],"lossless":[150],"near-lossless":[152],"compression.":[153],"Promising":[154],"throughput":[155],"results":[156],"are":[157],"also":[158],"evaluated":[159],"7-W":[162],"embedded":[163],"system.":[164]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7}],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}
