Abstract
In this work, we propose an algorithm for compressing lossless hyperspectral aerospace images, which is characterized by the use of a channel-difference linear regression transformation, which significantly reduces the range of data changes and increases the degree of compression. The main idea of the proposed conversion is to form a set of pairs of correlated channels with the subsequent creation of the transformed blocks without losses using regression analysis. This analysis allows you to reduce the size of the channels of the aerospace image and convert them before compression. The transformation of the regressed channel is performed on the values of the constructed regression equation model. An important step is coding with the adapted Huffman algorithm. The obtained comparison results of the converted hyperspectral AI suggest the effectiveness of the stages of regression conversion and multi-threaded processing, showing good results in the calculation of compression algorithms.
Highlights
Hyperspectral aerospace images (AI) are images obtained from Earth remote sensing spacecraft (ERS), designed to solve problems in the field of applied research
Y are unknown values, x are the values of the main channel, y are the values of the second channel, we find а and b: 1) we find a - coefficient by intermediate calculations; 2) we find b - coefficient by subtracting the average of y and the available coefficient а; 3) calculate Y = a x+b. where x - values of the main channel, found LRE
The lossless compression algorithm taking into account inter-band correlation and regression analysis allows to increase the compression ratio to (D>8)than in the use of universal archivers
Summary
Hyperspectral aerospace images (AI) are images obtained from Earth remote sensing spacecraft (ERS), designed to solve problems in the field of applied research. Based on the analysis of lossless GI compression methods and algorithms, it should be concluded that the most effective ways to solve the compression problem are: - taking into account spectral correlation, which gives certain advantages on the basis of the calculated correlation matrix; application of a new method of ordering GI channels; use of interpolation based on mathematical methods; arithmetic coding and the Huffman algorithm, which are the best among statistical methods; the use and organization of parallel compression processing to reduce the cost of computing resources. The proposed modification of the lossless compression algorithm taking into account inter-band correlation and regression analysis will increase the compression ratio by more than two times in comparison with the use of universal archivers. Compression by the Huffman statistical algorithm [2]
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