HYPERSPECTRAL IMAGE COMPRESSION ALGORITHMS for PHYTOSANITARY INSPECTION of AGRICULTURAL CROPS in AEROSPACE PHOTOGRAPHY
Sarinova A. Bekbayeva A. Dunayev P. Sarsikeyev Y. Sansyzbay K.
31 December 2021Little Lion Scientific
Journal of Theoretical and Applied Information Technology
2021#99Issue 246280 - 6290 pp.
The article presents studies of hyperspectral image compression algorithms for phytosanitary control of agricultural crops in aerospace photography. The existing algorithms for lossless compression of hyperspectral images are analyzed. In this paper, we propose an algorithm for lossless compression of hyperspectral aerospace images, characterized by the use of a channel-by-channel difference linear regression transformation, which significantly reduces the range of data changes and increases the compression ratio due to this. The main idea of the proposed transformation is to form a set of pairs of correlated channels with the subsequent creation of transformed lossless blocks using regression analysis. This analysis allows you to reduce the size of the channels of the aerospace image and transform them before compression. The transformation of the regressed channel is performed on the values of the constructed regression model of the equation. The obtained results of comparing the transformed hyperspectral AI allow us to assume the effectiveness of using the stages of regression preorazing, which shows good results when calculating compression algorithms.
Compression algorithm , Correlation , Hyperspectral aerospace images , Regression analysis
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Kazakh Agrotechnical University named after S. Seifullin, Nur-Sultan, Kazakhstan
Academy of Logistics and Transport, Almaty, Kazakhstan
Kazakh Agrotechnical University named after S. Seifullin
Academy of Logistics and Transport
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