COMPRESSION ALGORITHM OF THE HYPERSPECTRAL DATA OF EARTH REMOTE SENSING
Abstract
The evaluation results of hyperspectral data correlation in spatial and spectral domains are presented by the example of the hypercube AVIRIS Moffett Field, and the key features of hyperspectral data are formulated. The basic approaches to lossless compression and the algorithms, which can be applied in Earth remote sensing, are considerеd. They are the prediction (linear prediction, fast lossless, spectral oriented least squares, correlation-based conditional average prediction, M-CALIC), the lookup tables (lookup table, locally averaged interband scaling lookup tables), the 3D wavelets (3D-SPECK). A compression algorithm of hyperspectral data is proposed with regard to the advantages and disadvantages of specific implementations of the analyzed algorithms in remote sensing. The main algorithm stages are the preprocessing (for each spectral channel, it is executed independently), the reduction of a correlation level in the spectral area and the entropy coder. The test results of the developed algorithm are given in comparison to the alternative codecs on the AVIRIS test set (Cuprite, Jasper Ridge, Low Altitude, Moffet Field) that prove the efficiency of the proposed algorithm: parallel processing, low computing cost (low latency instructions are used, no division and multiplication), small random access memory requirements (the memory is used only for storage of the hypercube). In the context of the above advantages, the hardware implementation of the algorithm is allowed for on board the aircraft.
About the Authors
A. A. Doudkin
United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Minsk
Belarus
D. Sc. (Engineering), Head of the Laboratory
Belarus
D. Sc. (Engineering), Head of the Laboratory
D. Yu. Pertsau
Belarusian State University of Informatics and Radioelectronics, Minsk
Belarus
Junior Researcher
Belarus
Junior Researcher
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