Multi-resolution dictionary-based sparse coding method for interferometric phase image denoising

Abstract

Interferometric synthetic aperture radar (InSAR) is widely used in acquisition of digital elevation data as well as the settlement of Earth surface. Researching on the processing of space-born InSAR is of great importance in improving the accuracy of sensing data. The phase denoising method based on multi-resolution dictionary sparse coding (SC) is proposed. First, noisy blocks are extracted from the complex valued interferometric image. Then, the dictionary is trained by online method, which uses a portion of elements in the training set to update the dictionary column by column. And the trained dictionary is used to sparsely represent the noisy blocks by greedy method to denoise the image patches with different resolutions. The whole image of a given resolution is formed by combining the patches in the same resolution together. Finally, the denoising results of different resolutions are merged based on hypothesis testing and the phase estimations according to each pixels are computed. The experiments prove that the proposed denoising method based on multi-resolution SC achieves higher peak signal to noise ratio than traditional SC method.