Reconstruction Technique Based on the Theory of Compressed Sensing Satellite Images

ABSTRACT

Owing to the characteristics such as high resolution, large capacity, and great quantity, thus far, how to efficient store and transmit satellite images is still an unsolved technical problem. Satellite image Compressed sensing (CS) theory breaks through the limitations of traditional Nyquist sampling theory, it is based on signal sparsity, randomness of measurement matrix and nonlinear optimization algorithms to complete the sampling compression and restoring reconstruction of signal. This article firstly discusses the study of satellite image compression based on compression sensing theory. It then optimizes the widely used orthogonal matching pursuit algorithm in order to make it fits for satellite image processing. Finally, a simulation experiment for the optimized algorithm is carried out to prove this approach is able to provide high compression ratio and low signal to noise ratio, and it is worthy of further study.

Keywords:

Compressed Sensing, Optimization Algorithm, Random Sampling, Satellite Image Compression, Sparse representation.