With the development of remote sensing technology and the diversification of sensors, remote sensing image data reveals the trend of “three features” -- high resolution, hyper spectral and multi-temporal. As the increasing demand of remote sensing information, considerable amounts of data will be acquired, transmitted and stored in various remote sensing applications, which, without doubt, sets higher requirements for data processing. To solve the above problems, according to the feature of compressed sensing theory, which original image can be reconstructed by low sampling data, we develop a new method of Remote Sensing Image Block Compressed Sensing Reconstruction Based on Augmented Lagrangian Method TV. It represented remote sensing image sparsely by means of block sampling and joint sparse representation model. Besides, it also combined the total Variation and Augmented Lagrangian method to optimize the solution and implemented the algorithm of the model. Finally, it created a remote sensing image with low distortion. Furthermore, it also increased efficiency in data transmission and reduced data storage. Simulation test results confirm the validity of algorithm proposed in this paper and also suggest that it can achieve better effects of a distinct advantage in PSNR, which is remote sensing image reconstruction, in comparison with other algorithms.
Submitted on November 8, 2017; Revised on January 2, 2018; Accepted on February 3, 2018