Next Article in Journal
Prediction of Chlorophyll Content in Different Light Areas of Apple Tree Canopies based on the Color Characteristics of 3D Reconstruction
Next Article in Special Issue
A Sliding Window-Based Joint Sparse Representation (SWJSR) Method for Hyperspectral Anomaly Detection
Previous Article in Journal
A New Soil Moisture Downscaling Approach for SMAP, SMOS, and ASCAT by Predicting Sub-Grid Variability
Previous Article in Special Issue
Hyperspectral Classification Based on Texture Feature Enhancement and Deep Belief Networks
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessEditor’s ChoiceArticle
Remote Sens. 2018, 10(3), 428;

A New Algorithm for the On-Board Compression of Hyperspectral Images

Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Las Palmas, Spain
Author to whom correspondence should be addressed.
Received: 1 February 2018 / Revised: 27 February 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
PDF [13919 KB, uploaded 9 March 2018]


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 earth’s surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA), is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios. View Full-Text
Keywords: hyperspectral compression; lossy compression; on-board compression; orthogonal projections; Gram–Schmidt orthogonalization; parallel processing hyperspectral compression; lossy compression; on-board compression; orthogonal projections; Gram–Schmidt orthogonalization; parallel processing

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Guerra, R.; Barrios, Y.; Díaz, M.; Santos, L.; López, S.; Sarmiento, R. A New Algorithm for the On-Board Compression of Hyperspectral Images. Remote Sens. 2018, 10, 428.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top