Near-Lossless Compression of Remote-Sensing Data

This paper describes data compression algorithms capable to preserve the scientific quality of remote-sensing data, yet allowing a considerable bandwidth reduction to be achieved. Unlike lossless techniques, by which a moderate compression ratio (CR) is attainable, due to the intrinsic noisiness of the data, and conventional lossy techniques, in which the mean square error of the decoded data is globally controlled by the user, near-lossless methods are capable to locally constrain the maximum error, either absolute or relative, based on the user's requirements. Advanced near-lossless methods rely on differential pulse code modulation (DPCM) schemes, based on either prediction or interpolation. The latter is recommended for lower quality compression (i.e. higher CR), the former for higher-quality, which is the primary concern in remote sensing applications. Experimental results of near-lossless compression of multispectral, hyperspectral, and microwave data from coherent imaging systems, like synthetic aperture radar (SAR), show the advantages of the proposed approach compared to standard lossy techniques.