Abstract
The work is devoted to the description of the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery of 85 % or more, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table have been developed. The existing solutions to the lossless compression problem for hyperspectral aerospace images are analyzed. Based on them, a compression algorithm is proposed taking into account inter-channel correlation and the Walsh-Hadamard transformation, characterized by data transformation with a decrease in the range of the initial values by forming a set of channel groups [10–15] with high intra-group correlation [0.9–1] of the corresponding pairs with the selection of optimal parameters. The results obtained in the course of the research allow us to determine the optimal parameters for compression: the results of the compression ratio indicators were improved by more than 30 % with an increase in the size of the parameter channels. This is due to the fact that the more values to be converted, the fewer bits are required to store them. The best values of the compression ratio [8–12] are achieved by choosing the number of channels in an ordered group with high correlation.
Highlights
Modern satellite centers for space monitoring and remote sensing of the Earth (RSE) promptly receive, register, process, archive and distribute large amounts of data, sometimes amounting to hundreds of gigabytes
At the end of the research, it was noted that the efficiency in the compression ratio is achieved by applying the proposed compression algorithm, taking into account the correlation and ordering of channel groups and transformation based on discrete-cosine and Walsh-Hadamard
– the compression algorithm based on orthogonal discrete-cosine and Walsh-Hadamard transformations (Fig. 3, 4), taking into account the inter-band correlation, allows increasing the compression ratio to (D>8) compared to universal archivers and the JPEG Lossy algorithm (Fig. 5)
Summary
Modern satellite centers for space monitoring and remote sensing of the Earth (RSE) promptly receive, register, process, archive and distribute large amounts of data, sometimes amounting to hundreds of gigabytes. Hyperspectral AI RSE are important for observing and studying changes in the Earth’s surface, monitoring natural resources and the consequences of emergencies, etc. Hyperspectral AI are characterized by three features: spectral resolution, number of channels and inter-channel correlation. These signs were studied separately, which suggests their interaction. One of the key tasks in the field of remote sensing is the archiving of hyperspectral AI in order to increase the efficiency of data transmission over communication channels of limited bandwidth and their compression
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