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Keywords: remote sensing data compression; lossless compression; lossy compression; compression impact; neural networks; computational complexity Author: Benoit Vozel, Vladimir Lukin and Joan Serra-Sagristà Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Fri Sep 17 13:15:59 2021 CEST ModDate: Fri Sep 17 13:18:03 2021 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 5 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 215795 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- NBMIUS+VnURWPalladioL Type 1 Custom yes yes no 10 0 QHUFLH+URWPalladioL-Ital Type 1 Custom yes yes no 15 0 VWUFRN+URWPalladioL-Bold Type 1 Custom yes yes no 20 0 UFGBXT+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 WNWTFR+VnURWPalladioL-Bold Type 1 Custom yes yes no 28 0 ZKJPJD+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 36 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 58 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 62 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 66 0 AOPHAZ+CMSY10 Type 1 Builtin yes yes no 81 0 NRNYCF+EURM10 Type 1 Builtin yes yes no 88 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-11-30 02:37:38 CET RepresentationInformation: RemSen_a2021v13n18p3727.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-11-29 19:58:26 CET Size: 215795 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 142 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Overview of the Issue: Remote Sensing Data Compression Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4d3167f4 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@ed9d034 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@6121c9d6 Info: Title: Editorial to Special Issue “Remote Sensing Data Compression” Author: Benoit Vozel, Vladimir Lukin and Joan Serra-Sagristà Subject: A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance. 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