Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired o...

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Year of Publication:2021
Language:English
Physical Description:1 electronic resource (366 p.)
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collection bib_alma
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spelling Lukin, Vladimir edt
Remote Sensing Data Compression
Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
1 electronic resource (366 p.)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interesting
English
Technology: general issues bicssc
on-board data compression
CCSDS 123.0-B-2
near-lossless hyperspectral image compression
hyperspectral image coding
graph filterbanks
integer-to-integer transforms
graph signal processing
compact data structure
quadtree
k2-tree
k2-raster
DACs
3D-CALIC
M-CALIC
hyperspectral images
fully convolutional network
semantic segmentation
spectral image
tensor decomposition
HEVC
intra coding
JPEG 2000
high bit-depth compression
multispectral satellite images
crop classification
Landsat-8
Sentinel-2
Elias codes
Simple9
Simple16
PForDelta
Rice codes
hyperspectral scenes
hyperspectral image
lossy compression
real time
FPGA
PCA
JPEG2000
EBCOT
multispectral
hyperspectral
CCSDS
FAPEC
data compression
transform
hyperspectral imaging
on-board processing
GPU
real-time performance
UAV
parallel computing
remote sensing
image quality
image classification
visual quality metrics
spectral–spatial feature
multispectral image compression
partitioned extraction
group convolution
rate-distortion
compressed sensing
invertible projection
coupled dictionary
singular value
task-driven learning
on board compression
transform coding
learned compression
neural networks
variational autoencoder
complexity
real-time compression
on-board compression
real-time transmission
UAVs
compressive sensing
synthetic aperture sonar
underwater sonar imaging
remote sensing data compression
lossless compression
compression impact
computational complexity
3-0365-2303-0
3-0365-2304-9
Vozel, Benoit edt
Serra-Sagristà, Joan edt
Lukin, Vladimir oth
Vozel, Benoit oth
Serra-Sagristà, Joan oth
language English
format eBook
author2 Vozel, Benoit
Serra-Sagristà, Joan
Lukin, Vladimir
Vozel, Benoit
Serra-Sagristà, Joan
author_facet Vozel, Benoit
Serra-Sagristà, Joan
Lukin, Vladimir
Vozel, Benoit
Serra-Sagristà, Joan
author2_variant v l vl
b v bv
j s s jss
author2_role HerausgeberIn
HerausgeberIn
Sonstige
Sonstige
Sonstige
title Remote Sensing Data Compression
spellingShingle Remote Sensing Data Compression
title_full Remote Sensing Data Compression
title_fullStr Remote Sensing Data Compression
title_full_unstemmed Remote Sensing Data Compression
title_auth Remote Sensing Data Compression
title_new Remote Sensing Data Compression
title_sort remote sensing data compression
publisher MDPI - Multidisciplinary Digital Publishing Institute
publishDate 2021
physical 1 electronic resource (366 p.)
isbn 3-0365-2303-0
3-0365-2304-9
illustrated Not Illustrated
work_keys_str_mv AT lukinvladimir remotesensingdatacompression
AT vozelbenoit remotesensingdatacompression
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status_str n
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is_hierarchy_title Remote Sensing Data Compression
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