Exploiting the distributed source coding paradigm for low-complexity compression of hyperspectral images

The goal of this work is to study the feasibility of a low-complexity encoder for lossless compression of hyperspectral images. Since on-board bandwidth and power resources are limited for remote sensing systems, we adopted the distributed source coding (DSC) paradigm as a starting point for moving the computational complexity from the encoder to the decoder. The advantages from locating a simple encoder on the aerial platform are far more relevant than the increased costs of a more complex decoder at the ground station. Two lossless compression algorithms have been developed, the former performing a scalar encoding of the syndromes transmitted for each band of the hyperspectral image, the latter implementing a vectorial approach and yielding a slightly better compression ratio than the scalar one. No information about the spatial correlation is taken into account, while spectral correlation is explicitly exploited only at the decoder side. Experimental results confirm the asymmetrical distribution of computational complexity between encoder and decoder, with a strong increase of decoding times although the recorded encoding times are still higher than those achieved by JPEG-LS. As to the compression rate, our codecs perform very well compared to JPEG-LS or CALIC 2D, and worse than CALIC 3D, which also carries out inter-band decorrelation thus requiring a quite long processing time.