基于压缩感知理论的卫星云图重构技术研究

The Research on Satellite Image Reconstruction Based on Compressed Sensing Theory

压缩感知(Compressive Sensing, CS)理论突破了传统压缩编码技术中奈奎斯特(Nyquist)采样定理的限制,它基于信号的稀疏性、测量矩阵的随机性和非线性优化算法完成对信号的采样压缩和恢复重构。这种全新的理论为有效地克服传统压缩编码技术中的缺陷,解决高分辨率卫星云图压缩中面临的高采样率、大数据量和实时传输等困难提供了可能。本文概述了压缩感知的基本理论,详细探讨了基于压缩感知理论的卫星云图压缩研究,并对压缩感知理论中广泛应用的正交匹配追踪算法进行了优化使其更加适合于卫星云图的处理,并对优化后的算法对卫星云图的重建的效果进行了仿真实验,明确了研究中存在的问题,阐述了下一步的研究方向。

The theory of compressive sensing breaks the limit of traditional SyQuest sampling theory in-cluded traditional compression coding technology. It is based on the sparsity of the signal, the randomness of the measurement matrix and the nonlinear optimization algorithm to complete the sampling compression and reconstruction of the signal. This new theory can effectively overcome the shortcomings of traditional compression coding technology, and solve the difficulties of high sampling rate, large data volume and real-time transmission in high resolution satellite cloud compression. In this paper, the basic theory of compressed sensing is summarized, and the research of satellite image compression based on compressed sensing theory is discussed in detail;The compressed sensing theory is widely used in the orthogonal matching pursuit algorithm, which makes it more suitable for the processing of satellite imagery;The optimized algorithm of satellite cloud image simulation about the reconstruction effect is carried out, clear the problems in the research, and elaborate the research direction of the next step.