Next Article in Journal
The Boom in 3D-Printed Sensor Technology
Previous Article in Journal
Multispectral, Fluorescent and Photoplethysmographic Imaging for Remote Skin Assessment
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(5), 1160;

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU

Department of Electrical and Information Engineering, Politecnico di Bari, 70125 Bari, Italy
Author to whom correspondence should be addressed.
Academic Editor: Farid Melgani
Received: 28 March 2017 / Revised: 12 May 2017 / Accepted: 17 May 2017 / Published: 19 May 2017
(This article belongs to the Section Remote Sensors)
Full-Text   |   PDF [2318 KB, uploaded 19 May 2017]   |  


In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data needs to be reduced on board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature has focused the attention on efficient ways for on-board data compression. This topic is a challenging task due to the difficult environment (outer space) and due to the limited time, power and computing resources. Often, the hardware properties of Graphic Processing Units (GPU) have been adopted to reduce the processing time using parallel computing. The current work proposes a framework for on-board operation on a GPU, using NVIDIA’s CUDA (Compute Unified Device Architecture) architecture. The algorithm aims at performing on-board compression using the target’s related strategy. In detail, the main operations are: the automatic recognition of land cover types or detection of events in near real time in regions of interest (this is a user related choice) with an unsupervised classifier; the compression of specific regions with space-variant different bit rates including Principal Component Analysis (PCA), wavelet and arithmetic coding; and data volume management to the Ground Station. Experiments are provided using a real dataset taken from an AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) airborne sensor in a harbor area. View Full-Text
Keywords: hyperspectral imaging; region-of-interest; clustering; on-board compression; PCA; GPU hyperspectral imaging; region-of-interest; clustering; on-board compression; PCA; GPU

Figure 1

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

Giordano, R.; Guccione, P. ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU. Sensors 2017, 17, 1160.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top