Hyperspectral Data Compression Using Fully Convolutional Autoencoder

In space science and satellite imagery, better resolution of the data information obtained makes images clearer and interpretation more accurate. However, the huge data volume gained by the complex on-board satellite instruments becomes a problem that needs to be managed carefully. To reduce the dat...

Full description

Saved in:

Bibliographic Details
Published in:	Remote sensing (Basel, Switzerland) Vol. 14; no. 10; p. 2472
Main Authors:	La Grassa, Riccardo, Re, Cristina, Cremonese, Gabriele, Gallo, Ignazio
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.05.2022
Subjects:	Algorithms Approximation autoencoder Benchmarks Compression Compression ratio Computer vision Data compression Datasets Deep learning Image compression Image processing Image reconstruction Machine learning Neural networks Noise Remote sensing Satellite imagery satellite images Satellite instruments Satellites Signal reconstruction Wavelet transforms
ISSN:	2072-4292, 2072-4292
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	In space science and satellite imagery, better resolution of the data information obtained makes images clearer and interpretation more accurate. However, the huge data volume gained by the complex on-board satellite instruments becomes a problem that needs to be managed carefully. To reduce the data volume to be stored and transmitted on-ground, the signals received should be compressed, allowing a good original source representation in the reconstruction step. Image compression covers a key role in space science and satellite imagery and, recently, deep learning models have achieved remarkable results in computer vision. In this paper, we propose a spectral signals compressor network based on deep convolutional autoencoder (SSCNet) and we conduct experiments over multi/hyperspectral and RGB datasets reporting improvements over all baselines used as benchmarks and than the JPEG family algorithm. Experimental results demonstrate the effectiveness in the compression ratio and spectral signal reconstruction and the robustness with a data type greater than 8 bits, clearly exhibiting better results using the PSNR, SSIM, and MS-SSIM evaluation criteria.
AbstractList	In space science and satellite imagery, better resolution of the data information obtained makes images clearer and interpretation more accurate. However, the huge data volume gained by the complex on-board satellite instruments becomes a problem that needs to be managed carefully. To reduce the data volume to be stored and transmitted on-ground, the signals received should be compressed, allowing a good original source representation in the reconstruction step. Image compression covers a key role in space science and satellite imagery and, recently, deep learning models have achieved remarkable results in computer vision. In this paper, we propose a spectral signals compressor network based on deep convolutional autoencoder (SSCNet) and we conduct experiments over multi/hyperspectral and RGB datasets reporting improvements over all baselines used as benchmarks and than the JPEG family algorithm. Experimental results demonstrate the effectiveness in the compression ratio and spectral signal reconstruction and the robustness with a data type greater than 8 bits, clearly exhibiting better results using the PSNR, SSIM, and MS-SSIM evaluation criteria.
Author	Gallo, Ignazio Re, Cristina Cremonese, Gabriele La Grassa, Riccardo
Author_xml	– sequence: 1 givenname: Riccardo orcidid: 0000-0002-4355-0366 surname: La Grassa fullname: La Grassa, Riccardo – sequence: 2 givenname: Cristina surname: Re fullname: Re, Cristina – sequence: 3 givenname: Gabriele orcidid: 0000-0001-9021-1140 surname: Cremonese fullname: Cremonese, Gabriele – sequence: 4 givenname: Ignazio orcidid: 0000-0002-7076-8328 surname: Gallo fullname: Gallo, Ignazio
BookMark	eNptkF1LwzAYhYNMcM7d-AsK3gnVfDVpLsfm3GDgjbsOaZKOjq6pSSrs35s5RRFzk3B4znnfnGsw6lxnAbhF8IEQAR99QBRBTDm-AGMMOc4pFnj0630FpiHsYTqEIAHpGCxWx9760FsdvWqzhYoqm7tD720IjeuybWi6XbYc2vaY9O7dtUNMekJnQ3S2085YfwMua9UGO_26J2C7fHqdr_LNy_N6PtvkmjAU86KgpRG8rqoC0YrWjFOstCg55ErVzJSkrBAyuMC65IbWpLKCJlBowxgXhkzA-pxrnNrL3jcH5Y_SqUZ-Cs7vpPKx0a2V1BCmBEUE65ShdIkY5BYyYWGZJtmUdXfO6r17G2yIcu8Gnz4WJGYcEsHLokgUPFPauxC8raVuojo1kOpqWomgPFUvf6pPlvs_lu9F_4E_AOO2hH4
CitedBy_id	crossref_primary_10_1109_TGRS_2023_3328222 crossref_primary_10_1016_j_sigpro_2025_110270 crossref_primary_10_1109_OJCOMS_2025_3537105 crossref_primary_10_1109_TGRS_2024_3509718 crossref_primary_10_1007_s41870_024_02244_8 crossref_primary_10_1051_epjconf_202429509023 crossref_primary_10_1109_TGRS_2024_3465043 crossref_primary_10_1109_ACCESS_2024_3456914 crossref_primary_10_1016_j_foodcont_2025_111606 crossref_primary_10_3390_rs16173258 crossref_primary_10_3390_electronics13091700 crossref_primary_10_1007_s11831_025_10260_5 crossref_primary_10_1109_TGRS_2024_3483312 crossref_primary_10_1109_TGRS_2024_3483871 crossref_primary_10_1117_1_JRS_17_046509 crossref_primary_10_1038_s41598_025_96282_2 crossref_primary_10_1109_TGRS_2025_3596240 crossref_primary_10_1109_JSTARS_2025_3584931 crossref_primary_10_1016_j_knosys_2025_112996 crossref_primary_10_3390_rs15184422 crossref_primary_10_1016_j_jfranklin_2024_107056 crossref_primary_10_1109_TGRS_2023_3282186 crossref_primary_10_1109_LGRS_2025_3554269 crossref_primary_10_3390_rs15184460 crossref_primary_10_1007_s11242_025_02190_4
Cites_doi	10.1007/978-3-030-58539-6_11 10.3390/rs13050839 10.3390/ijgi10070483 10.1109/ICCVW54120.2021.00217 10.1109/TCSVT.2020.3010627 10.1109/TIP.2020.2970248 10.3390/rs12213657 10.1145/3158369 10.2514/6.2016-5539 10.3390/rs11121417 10.1016/j.oregeorev.2018.10.006 10.1007/s11214-006-9127-5 10.1109/PCS.2018.8456308 10.1109/LGRS.2015.2499239 10.1016/j.neucom.2021.11.022 10.1109/TIP.2003.819861 10.1146/annurev-bioeng-071516-044442 10.1109/30.125072 10.1109/CVPR.2017.577 10.1109/ACCESS.2018.2818280 10.1145/3194085.3194087 10.1007/978-3-642-35289-8_3 10.1038/d41586-018-02174-z 10.1109/30.920468
ContentType	Journal Article
Copyright	2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F28 FR3 H8D H8G HCIFZ JG9 JQ2 KR7 L6V L7M L~C L~D M7S P5Z P62 P64 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS DOA
DOI	10.3390/rs14102472
DatabaseName	CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Ecology Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection Natural Science Collection Earth, Atmospheric & Aquatic Science Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Materials Research Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences Engineered Materials Abstracts Natural Science Collection Chemoreception Abstracts ProQuest Central (New) Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest One Academic Eastern Edition Electronics & Communications Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Ceramic Abstracts Ecology Abstracts Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Engineering Collection Biotechnology Research Abstracts ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection Corrosion Abstracts
DatabaseTitleList	Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography
EISSN	2072-4292
ExternalDocumentID	oai_doaj_org_article_4d36a94132c944ac81607e069e086d8e 10_3390_rs14102472
GroupedDBID	29P 2WC 2XV 5VS 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ABJCF ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION E3Z ESX FRP GROUPED_DOAJ HCIFZ I-F IAO ITC KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PQGLB PROAC PTHSS TR2 TUS 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD ABUWG AZQEC C1K DWQXO F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c361t-5548d97fbb514b4f6742ac98707aaf6d838b11d252c87d4f3be944b49cd6679d3
IEDL.DBID	DOA
ISICitedReferencesCount	36
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000801407300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2072-4292
IngestDate	Mon Nov 10 04:35:09 EST 2025 Fri Jul 25 09:34:45 EDT 2025 Sat Nov 29 07:12:24 EST 2025 Tue Nov 18 22:38:32 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	10
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c361t-5548d97fbb514b4f6742ac98707aaf6d838b11d252c87d4f3be944b49cd6679d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-4355-0366 0000-0001-9021-1140 0000-0002-7076-8328
OpenAccessLink	https://doaj.org/article/4d36a94132c944ac81607e069e086d8e
PQID	2670397855
PQPubID	2032338
ParticipantIDs	doaj_primary_oai_doaj_org_article_4d36a94132c944ac81607e069e086d8e proquest_journals_2670397855 crossref_citationtrail_10_3390_rs14102472 crossref_primary_10_3390_rs14102472
PublicationCentury	2000
PublicationDate	2022-05-01
PublicationDateYYYYMMDD	2022-05-01
PublicationDate_xml	– month: 05 year: 2022 text: 2022-05-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Remote sensing (Basel, Switzerland)
PublicationYear	2022
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	ref_14 ref_36 ref_13 ref_35 ref_12 ref_34 Zhang (ref_11) 2019; 52 ref_33 ref_10 ref_32 ref_30 Marmanis (ref_16) 2015; 13 Sun (ref_4) 2020; 29 ref_19 ref_15 Christopoulos (ref_18) 2000; 46 ref_37 Mishra (ref_21) 2020; 31 Palsson (ref_23) 2018; 6 Shen (ref_6) 2017; 19 Webb (ref_5) 2018; 554 Wallace (ref_17) 1992; 38 ref_25 ref_24 ref_22 ref_20 Gallo (ref_1) 2022; 470 ref_3 ref_2 ref_29 ref_28 ref_26 Wang (ref_38) 2004; 13 ref_9 Vahdat (ref_27) 2020; 33 ref_8 Xiong (ref_7) 2018; 102 Coradini (ref_31) 2007; 128
References_xml	– ident: ref_28 – ident: ref_30 – ident: ref_2 doi: 10.1007/978-3-030-58539-6_11 – ident: ref_15 doi: 10.3390/rs13050839 – ident: ref_32 – ident: ref_8 doi: 10.3390/ijgi10070483 – ident: ref_3 doi: 10.1109/ICCVW54120.2021.00217 – ident: ref_26 – ident: ref_34 – volume: 31 start-page: 1452 year: 2020 ident: ref_21 article-title: Wavelet-based deep auto encoder-decoder (wdaed)-based image compression publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2020.3010627 – volume: 29 start-page: 4027 year: 2020 ident: ref_4 article-title: Learned image downscaling for upscaling using content adaptive resampler publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2020.2970248 – ident: ref_24 doi: 10.3390/rs12213657 – volume: 33 start-page: 19667 year: 2020 ident: ref_27 article-title: Nvae: A deep hierarchical variational autoencoder publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_37 – ident: ref_14 – volume: 52 start-page: 1 year: 2019 ident: ref_11 article-title: Deep learning based recommender system: A survey and new perspectives publication-title: ACM Comput. Surv. doi: 10.1145/3158369 – ident: ref_9 doi: 10.2514/6.2016-5539 – ident: ref_36 doi: 10.3390/rs11121417 – volume: 102 start-page: 811 year: 2018 ident: ref_7 article-title: Mapping mineral prospectivity through big data analytics and a deep learning algorithm publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2018.10.006 – volume: 128 start-page: 529 year: 2007 ident: ref_31 article-title: VIRTIS: An imaging spectrometer for the Rosetta mission publication-title: Space Sci. Rev. doi: 10.1007/s11214-006-9127-5 – ident: ref_13 doi: 10.1109/PCS.2018.8456308 – volume: 13 start-page: 105 year: 2015 ident: ref_16 article-title: Deep learning earth observation classification using ImageNet pretrained networks publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2015.2499239 – volume: 470 start-page: 217 year: 2022 ident: ref_1 article-title: σ2R loss: A weighted loss by multiplicative factors using sigmoidal functions publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.11.022 – volume: 13 start-page: 600 year: 2004 ident: ref_38 article-title: Image quality assessment: From error visibility to structural similarity publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2003.819861 – ident: ref_29 – ident: ref_33 – ident: ref_12 – volume: 19 start-page: 221 year: 2017 ident: ref_6 article-title: Deep learning in medical image analysis publication-title: Annu. Rev. Biomed. Eng. doi: 10.1146/annurev-bioeng-071516-044442 – volume: 38 start-page: xviii year: 1992 ident: ref_17 article-title: The JPEG still picture compression standard publication-title: IEEE Trans. Consum. Electron. doi: 10.1109/30.125072 – ident: ref_25 doi: 10.1109/CVPR.2017.577 – volume: 6 start-page: 25646 year: 2018 ident: ref_23 article-title: Hyperspectral unmixing using a neural network autoencoder publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2818280 – ident: ref_10 doi: 10.1145/3194085.3194087 – ident: ref_35 doi: 10.1007/978-3-642-35289-8_3 – volume: 554 start-page: 555 year: 2018 ident: ref_5 article-title: Deep learning for biology publication-title: Nature doi: 10.1038/d41586-018-02174-z – volume: 46 start-page: 1103 year: 2000 ident: ref_18 article-title: The JPEG2000 still image coding system: An overview publication-title: IEEE Trans. Consum. Electron. doi: 10.1109/30.920468 – ident: ref_19 – ident: ref_22 – ident: ref_20
SSID	ssj0000331904
Score	2.5055184
Snippet	In space science and satellite imagery, better resolution of the data information obtained makes images clearer and interpretation more accurate. However, the...
SourceID	doaj proquest crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	2472
SubjectTerms	Algorithms Approximation autoencoder Benchmarks Compression Compression ratio Computer vision Data compression Datasets Deep learning Image compression Image processing Image reconstruction Machine learning Neural networks Noise Remote sensing Satellite imagery satellite images Satellite instruments Satellites Signal reconstruction Wavelet transforms
SummonAdditionalLinks	– databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1La9wwEB7STaG5NOmLbpMWQ3PpwcSWZD1OpXmRQ1lCSUJuRtajLRRv6nUC-feZsbUbSkouufggD8Z4RqPvk0ffAOxGFUofbcyjVzIXFi-a2ZBLXtiC-UaXQ9O-i-9qNtOXl-Y0bbgtUlnlMicOidrPHe2R7zGJsYmUp6q-Xv3NqWsU_V1NLTSewToplYkJrO8fzU5_rHZZCo4hVohRl5Qjv9_rFlTZyIRi_6xEg2D_g3w8LDLHm099vS14meBl9m2Mh1ewFtrX8CJ1Ov91-wYOT5B4jucrOzQ8tL3NKCeM5bBtNpQQZERMb3G8vUmRSc-87uekeulD9xbOj4_ODk7y1Ekhd1yWfY6YQXujYtMgPmpElEiIrTM4V5W1UXrNdVOWnlXMaeVF5E0wAg2N81Iq4_k7mLTzNryHjGlCGVUIVUSoJRQSXAJ1zCBU0q7kU_iy_Kq1SzLj1O3iT410gzxQ33tgCp9XtlejuMZ_rfbJOSsLEsQeBubdzzrNr1p4Lq3BFZk5fHPrNAnnhUKagJzN6zCFnaXf6jRLF_W90z48fnsbNhgdexgKHXdg0nfX4SM8dzf970X3KQXdHSXC4Ak priority: 102 providerName: ProQuest
Title	Hyperspectral Data Compression Using Fully Convolutional Autoencoder
URI	https://www.proquest.com/docview/2670397855 https://doaj.org/article/4d36a94132c944ac81607e069e086d8e
Volume	14
WOSCitedRecordID	wos000801407300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: DOA dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: P5Z dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PCBAR dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M7S dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: BENPR dateStart: 20090301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PIMPY dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NS9xAFB9EhfYiVVu6dbsE9NJD2M3MZD6O1V1ZQZdgVWwvYTIfKEi2ZLMLe-nf3veSaC0WvPQyh8kjH29m3vx-5M3vEXIUpE9cMCEOToqYG2gUNT4WbGRG1BUqaYr23ZzL2Uzd3ursWakvzAlr5YFbxw25Y8JoCLXUas6NVaiI5kdCewDjTnmMvoB6npGpJgYzmFoj3uqRMuD1w2qBGY2US_rXDtQI9b-Iw83mcvqO7HSoMPravs0u2fDlHnnTFSi_W--T8RT4YnsssgLDsalNhEu5zWIto-bPf4R8cg395aqbUHjPZT1HsUrnq_fk-nRydTKNuwIIsWUiqWPY6pXTMhQFwJqCBwE81lgNS0waE-DrmSqSxNGUWiUdD6zw4KOCa-uEkNqxD2SznJf-I4moQnCQep8GQEhcAi9FLEY1IBxlE9YjXx6dkttOHRyLVDzkwBLQgfkfB_bI4ZPtz1YT459Wx-jbJwvUsW46YHTzbnTz10a3R_qPI5N3i2uRUwFhCthvmn76H884IG8pnmloshj7ZLOulv4z2bar-n5RDcjW8WSWXQ6a-TXA1NBv2P6aQJulP-B6dnaRff8NY1TWAg
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFKm9lLeaUsAScOBg1d5d7-OAEBCqRE2jHAoqJ7PeB62EnOK4RflT_EZm_UiFQNx64OKDPVrZns-z37eenQF44YVLrdc-9lbwmGk8SKJdzGmiE2ILmTZN-z5NxWwmT0_VfAN-9nthQlplHxObQG0XJqyRHxCO2ETJk2VvLr7HoWtU-Lvat9BoYXHkVj9Qsi1fT0bo35eEHH44eT-Ou64CsaE8rWOcP6VVwhcFcoWCeY7iUBuU3onQ2nMrqSzS1JKMGCks87RwiqGhMpZzoSzFcW_BJkOwywFszifH88_rVZ2EIqQT1tZBpVQlB9UyZFISJshvM1_TIOCP-N9Maod3_rfXcRd2OvocvW3xfg82XHkftrpO7merBzAao7Bu949WaDjStY5CzGvTfcuoSZGIgvBe4fnyqvvywpiX9SJU9bSueggfb-QhHsGgXJRuFyIiA4vKnMs8UkkmUMAH0koUUkFpUjqEV70Xc9OVUQ_dPL7lKKeCx_Nrjw_h-dr2oi0e8lerdwEMa4tQ8Ls5sai-5l38yJmlXCtkHMTgnWsjQ2FAl3DlUJNa6Yaw3-Mk76LQMr8Gyd6_Lz-DrfHJ8TSfTmZHj2GbhC0eTVLnPgzq6tI9gdvmqj5fVk87wEfw5aZB9QvfIDy3
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VbQW9QMtDbCkQqXDgEG1iJ34cEGq7rFq1Wq0QoN6C4wcgoWybTVvtX-PXMU6crRCIWw9ccnBGVpL5PDOfM54BeOW4TY1TLnaGszhTeBFE2ZjRRCXElCJtm_Z9PuXTqTg7k7M1-NmfhfFplb1NbA21mWu_Rz4iDLGJlCfPRy6kRczGk3fnF7HvIOX_tPbtNDqInNjlNdK3xdvjMer6NSGT9x8Pj-LQYSDWlKVNjL5UGMldWWLcUGaOIVFUGml4wpVyzAgqyjQ1JCdacJM5WlqZoaDUhjEuDcV578C6wGcjA1ifHR7sf1jt8CQU4Z1kXU1USmUyqhc-q5JknPzmBdtmAX_4gtbBTR78z59mC-6HsDra79bBNqzZ6iHcCx3evy0fwfgICXd3rrRGwbFqVORtYZcGXEVt6kTkCfkSx6ursCL9nJfN3Ff7NLZ-DJ9u5SWewKCaV_YpRET46Cq3NncYYmYcib0PZonEEFHolA7hTa_RQofy6r7Lx48CaZbXfnGj_SHsrWTPu6Iif5U68MBYSfhC4O3AvP5aBLtSZIYyJTESIRqfXGnhCwbahEmLXNUIO4TdHjNFsE6L4gYwO_--_RLuIpKK0-PpyTPYJP7kR5vruQuDpr60z2FDXzXfF_WLgP0Ivtw2pn4BQ_JFJw
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Hyperspectral+Data+Compression+Using+Fully+Convolutional+Autoencoder&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.au=Riccardo+La+Grassa&rft.au=Cristina+Re&rft.au=Gabriele+Cremonese&rft.au=Ignazio+Gallo&rft.date=2022-05-01&rft.pub=MDPI+AG&rft.eissn=2072-4292&rft.volume=14&rft.issue=10&rft.spage=2472&rft_id=info:doi/10.3390%2Frs14102472&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_4d36a94132c944ac81607e069e086d8e
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon