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 a compression ratio (CR) is attainable, due to intrinsic noisiness of the data, and conventional lossy techniques, in which the mean squared error of the decoded data is globally controlled by 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. Goal of this work is to investigate and compare different compression methodologies from the viewpoint of spectral distortion introduced in hyperspectral pixel vectors. The main result of this analysis is that, for a given compression ratio, near-lossless methods, having constrained pixel error, either absolute or relative, are more suitable for preserving the spectral discrimination capability among pixel vectors, which is the principal source of spectral information. Therefore, whenever a lossless compression is not practicable, the use of near-lossless compression is recommended in such application where spectral quality is a crucial point.