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Proceedings Paper

Lossy hyperspectral image compression with state-of-the-art video encoder
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Paper Abstract

One of the main drawbacks encountered when dealing with hyperspectral images is the vast amount of data to process. This is especially dramatic when data are acquired by a satellite or an aircraft due to the limited bandwidth channel needed to transmit data to a ground station. Several solutions are being explored by the scientific community. Software approaches have limited throughput performance, are power hungry and most of the times do not match the expectations needed for real time applications. Under the hardware point of view, FPGAs, GPUs and even the Cell Processor, represent attractive options, although they present complex solutions and potential problems for their on-board inclusion. However, sometimes there is an impetus for developing new architectural and technological solutions while there is plenty of work done in the past that can be exploited for solving drawbacks in the present. In this scenario, H.264/AVC arises as the state-of-the-art standard in video coding, showing increased compression efficiency with respect to any previous standard, and although mainly used for video applications, it is worthwhile to explore its convenience for processing hyperspectral imaginery. In this work, an inductive exercise of compressing hyperspectral cubes with H.264/AVC is carried out. An exhaustive set of simulations have been performed, applying this standard locally to each spectral band and evaluating globally the effect of the quantization factor, QP, in order to determine an optimum configuration of the baseline encoder for INTRA prediction modes. Results are presented in terms of spectral angle as a metric for determining the feasibility of the endmember extraction. These results demonstrate that under certain assumptions, the use of standard video codecs represent a good compromise solution in terms of complexity, flexibility and performance.

Paper Details

Date Published: 12 October 2011
PDF: 11 pages
Proc. SPIE 8183, High-Performance Computing in Remote Sensing, 81830G (12 October 2011); doi: 10.1117/12.898523
Show Author Affiliations
Lucana Santos, Univ. de Las Palmas de Gran Canaria (Spain)
Sebastian López, Univ. de Las Palmas de Gran Canaria (Spain)
Gustavo M. Callicó, Univ. de Las Palmas de Gran Canaria (Spain)
Jose F. López, Univ. de Las Palmas de Gran Canaria (Spain)
Roberto Sarmiento, Univ. de Las Palmas de Gran Canaria (Spain)


Published in SPIE Proceedings Vol. 8183:
High-Performance Computing in Remote Sensing
Bormin Huang; Antonio J. Plaza, Editor(s)

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