Abstract

The article is devoted to developing a compression method using context modeling of a sequence of bits and wavelet transform, which make it possible to take into account the specifics and properties of the initial hyperspectral remote sensing data. Two algorithms for compressing hyperspectral data (lossy and lossless) based on wavelet transform are proposed, the distinguishing features of which are reduction in the required memory size, acceleration of the search for significant wavelet coefficients using a pyramid with approximating coefficients, and an increase in the compression coefficient. Recommendations for applying these algorithms are formulated. A distinctive feature of the hyperspectral data compression method is the ability to control the compression coefficient owing to parametric adjustment of the algorithms, application of context modeling and adaptation to the type of initial data (classical cube or Fourier interferogram). The efficiency of the technique has been experimentally confirmed using examples of compression of classical data and real Fourier interferograms with compression ratios of 4.1 and 2.4, corresponding to the level of the best global results, as well as analytically with data distortion in a compressed stream.

中文翻译：

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高光谱遥感数据压缩问题中的上下文建模

摘要

本文致力于使用位序列的上下文建模和小波变换来开发一种压缩方法，从而可以考虑初始高光谱遥感数据的特性和特性。提出了两种基于小波变换的压缩高光谱数据（有损和无损）的算法，它们的显着特征是所需的存储大小减少，使用具有近似系数的金字塔加快搜索重要的小波系数以及增加压缩系数。提出了应用这些算法的建议。高光谱数据压缩方法的显着特征是由于算法的参数调整而能够控制压缩系数，应用上下文建模并适应初始数据的类型（经典立方体或傅立叶干涉图）。该技术的效率已通过使用经典数据的压缩示例以及压缩比为4.1和2.4的实际傅立叶干涉图的示例进行了实验证实，这对应于最佳全局结果的水平，并且在压缩流中分析了数据失真。