Future high resolution instruments planned by CNES for space remote sensing missions will lead to higher bit rates
because of the increase in resolution, dynamic range and number of spectral channels for multispectral (up to 16 bands)
and hyperspectral (hundreds of bands) imagery. Lossy data compression is then needed, with compression ratio goals
always higher and with low-complexity algorithm. For optimum compression performance of such data, algorithms must
exploit both spectral and spatial correlation. In the case of multispectral images, CNES (in cooperation with Thales
Alenia Space, hereafter TAS) studies have led to an algorithm using a fixed transform to decorrelate the spectral bands,
the CCSDS codec compresses each decorrelated band using a suitable multispectral rate allocation procedure. This low-complexity
decorrelator is adapted to hardware implementation on-board satellite and is under development. In the case
of hyperspectral images, CNES (in cooperation with TAS/TeSA/ONERA) studies have led to a full wavelet compression
system followed by zerotree coding methods adapted to this decomposition. We are investigating other preprocessors
such as Independent Component Analysis which could be used in both approaches. CNES also participates to the new
CCSDS Multispectral and Hyperspectral Data Compression Working Group.
Hyperspectral data appears to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy. Handling the significant size of hyperspectral data presents a challenge for the user community. To enable efficient data compression without losing the potentiality of hyperspectral data, the notion of data quality is crucial for the development of applications. To assess the data quality, quality criteria relevent to end-user applications are required.
This paper proposes a method to evaluate quality criteria. The purpose is to provide quality criteria corresponding well to the impact of degradation on end-user applications. Several quality criteria adapted to hyperspectral context are evaluated. Finally, five criteria are selected to give a good representation of the degradation nature and level affecting hyperspectral data.