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 lowcomplexity 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.
Future high resolution instruments planned by CNES for space remote sensing missions will lead to higher bit rates because of the increase in both resolution and dynamic range (bits per pixel), not compensated by the reduced swath. Data compression is then needed, with compression ratio goals always higher and with remaining unnoticeable artifacts. Moreover, space-borne earth observation instruments have several spectral channels (one panchromatic band and several spectral bands) which are generally processed independently, the spectral correlation being ignored. However, for optimum compression performance, algorithms must exploit both spectral and spatial correlation; such algorithms are called "multispectral". This paper proposes a low complexity and flexible fixed data rate compression algorithm for multispectral imagery specifically targeted for high-rate instruments used onboard of spacecraft. Moreover, to reduce the development costs, this algorithm re-use, as far as possible, already existing compressors, such as CNES algorithms already used in previous missions or standard algorithms suited for space missions. The proposed algorithm is then composed of a decorrelation stage performed on spectral channels and can be used in front of existing monospectral compressors. The decorrelation stage is based either on a fixed transform evaluated previously on a set of images or on the Karhunen-Loeve Transform locally adapted to the studied block of image, implemented in a scan-based mode [1]. Performances have been obtained with two CNES algorithms: the one presented in [2] and the CNES/Astrium algorithm developed for the PLEIADES-HR instrument. It is also planned to use it with the CCSDS Lossy Image Compression Algorithm [3].For registered images and for the same image quality, compression ratio gain is around 30-40% with multispectral compression which means a gain of 4 dB for the same compression rate. For non-registered images with high level of non-registration, this gain is lost but performances are not lower than in monospectral case. The implementation complexity of both spectral decorrelators and of an on-board registration algorithm is an on-going work. Further studies will lead to the development of a hardware implementation of both registration algorithm and decorrelation stage. It is planned to use it with the CCSDS algorithm hardware implementation. In order to have a significant gain in terms of compression rate, it would be preferable to compress simultaneously panchromatic and spectral bands.
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