The volume of image data generated by airborne and spaceborne remote sensing mission have been increased dramatically. The efficient lossless compression is urgent. A hyperspectral image comprises a number of bands

each of which represents the intensity of return from an imaged scene received by a sensor at a particular wavelength. Since the reflectance of the earth’s surface and atmospheric absorption are wavelength dependent

the brightness vector formed in the spectral domain for each pixel will have a similar form. The relationship between type of ground and spectral response means that a hyperspectral image can be considered as a group of brightness vectors. Therefore VQ（vector quantization） an the ideal candidate for compression. If VQ is used to compress image losslessly

both the codevector index and the quantization error image should be sent to channel. The amount of codevector index is invariable

consequently

it is important to reduce the error image’s average information amount

i.e. entropy

if we want to improve the coding efficiency. In this paper

a new VQ lossless compression method based on an information distortion measure is proposed. Using this new measure to match codevector

i.e. quantize vector

the coding efficiency can be improved without increasing complexity. Experimental results show that the entropy of the error image using VQ based on information distortion measure is about 0.05bpp （bits per pixel） lower than that on Euclidean square error measure.