Abstract
Methodology for Developing Algorithms for Compressing Hyperspectral Aerospace Images Used on Board Spacecraft
Highlights
Hyperspectral remote sensing aerospace images (AI) is important for observing and studying changes in the Earth's surface, monitoring natural resources and the consequences of emergencies, etc
There are two areas of research: the development of compression algorithms used in ground-based remote sensing data reception and processing centers; and those used on Board SPACECRAFT
It is proposed to formulate some problems of developing algorithms for compressing hyperspectral AI that are applicable on Board
Summary
Hyperspectral remote sensing AI is important for observing and studying changes in the Earth's surface, monitoring natural resources and the consequences of emergencies, etc. The development of software systems for transmitting such data is an urgent task In solving this problem, there are two areas of research: the development of compression algorithms used in ground-based remote sensing data reception and processing centers; and those used on Board SPACECRAFT. The problems listed above that arise when developing software systems that are applicable on Board the SPACECRAFT give rise to the following range of requirements for the compression algorithm:. Well-known algorithms based on truncated block encoding [1], differential pulse modulation [1], discrete cosine transform [2], and discrete wavelet transform [2] exist and are used for compressing hyperspectral AI These algorithms on the SC bot, which require large computing resources, do not always meet the above compression requirements. In this paper, we propose to consider a method for developing algorithms for compressing hyperspectral AI that meet these requirements
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