ABSTRACT
This chapter describes a category of data-compression algorithms capable of preserving the scientific quality of remote-sensing data, yet allowing a considerable reduction of the transmission bandwidth. Lossless compression applied to remote-sensing images guarantees only a moderate reduction of the data volume, because of the intrinsic noisiness of the data; on the other hand, conventional lossy techniques, in which the mean-squared error (MSE) of the decoded data is globally controlled by users, generally does not preserve the scientific quality of the images. The most suitable approach seems to be the use of near-lossless methods, which are capable of locally constraining the maximum error, either absolute or relative, based on the user’s requirements. Advanced near-lossless methods may rely on differential pulse code modulation (DPCM) schemes, based on either interpolation or prediction. The former is recommended for lower quality compression, the latter for higher quality, which is the primary concern in remote-sensing applications. Experimental results of near-lossless compression ofmultispectral, hyperspectral, andmicrowavedata fromcoherent imaging systems, like synthetic aperture radar (SAR), show the advantages of the proposed approach compared to standard lossy techniques.
