The signature of hyperspectral image (HSI) pixels and their decomposition into empirical modes (EM) and low-frequency residuals are investigated. On the basis of estimates related to the EM-decomposition method, the possibility of switching from a 2-byte representation of the values of the HIS-signature to a 1-byte one is examined using the example of the Moffett Field from the AVIRIS spectrometer. It is revealed that the localization of the minimum window sizes for the first EM is correlated with the localization of the significant influence of the atmosphere; the first low-frequency residues have a fairly high correlation coefficient with the signature and the first 2 of them and their EM are most interesting for use; 50 of the 224 HIS-channels are noisy and can be excluded from consideration; EM with practically no loss of accuracy can be reduced to a 1-byte representation. The management of the classification capabilities of signatures by changing the threshold value of the correlation coefficient with the sample, as well as the application of the 1st and 2nd low-frequency residues in place of the signature, was studied. Classification capabilities of signatures in a 1byte representation are almost equivalent to a 2-byte one, which makes it possible to put a signature with 1-byte representation as the object of compression. For the wavelet decomposition of the HSI data array, in combination with a 1-byte representation, a nearlossless compression ratio of 6.65 is obtained.

The work is a continuation of the authors' research on the problem of adaptive compression of raster hyperspectral images of Earth remote sensing. In the first part of the article, the authors give an overview of the current state of affairs in the processing of images of remote sensing of the Earth, the characteristic properties of raster hyperspectral images in the context of the prospects for lossy compression, the problems of the effectiveness of existing compression methods of this type of graphic documents are indicated. Further, the article highlights the issues of increasing the efficiency of methods for eliminating information redundancy of raster hyperspectral images of Earth remote sensing. The problems of designing and creating parallel methods and algorithms for the compression of raster hyperspectral ERS images are considered. A method for the development of a parallel algorithm for constructing a system of local homogeneous "well-adapted" basis functions for raster hyperspectral images, based on the Chebyshev approximation for systems using the CUDA graphics processor, is proposed.

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