Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/70065
Title: A Hardware-Friendly Algorithm for the On-Board Compression of Hyperspectral Images
Authors: Guerra, Raul 
Diaz, Maria
Barrios, Yubal
Lopez, Sebastian 
Sarmiento, Roberto 
Keywords: Hyperlca Compressor
Hyperspectral Compression
Integer Arithmetic
On-Board Compression
Issue Date: 2018
Journal: Workshop On Hyperspectral Image And Signal Processing, Evolution In Remote Sensing
Abstract: © 2018 IEEE. Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the Earths surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Additionally, the computational capabilities of hardware devices available on-board satellites is limited, being its efficiency typically affected by the data types required by the compression algorithms. This work evaluates the precision required by the Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA) compressor in order to execute it using just integer arithmetic operations without decreasing its compression rate-distortion performance. The goal is to make the HyperLCA compressor more suitable for on-board hardware processors, which are more efficient executing integer operations than floating point operations.The proposed integer version of the HyperLCA compressor has been tested using different hyperspectral images, comparing the obtained results with those provided by its floating point version. The obtained results verify that the HyperLCA compressor can be programmed using just integer arithmetic operations in order to provide the same compression results than when using floating point precision, what easies its implementation in many hardware devices.
URI: http://hdl.handle.net/10553/70065
ISBN: 9781728115818
ISSN: 2158-6276
DOI: 10.1109/WHISPERS.2018.8747229
Source: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing[ISSN 2158-6276],v. 2018-September
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