Sparse representation for remote sensing images of long time sequences

Adaptive sparse representations of signals have drawn considerable interest in the past decade. In this paper, we address the problem of training dictionaries for massive images and propose a new algorithm for adapting dictionaries by extending the classical K-SVD based on only a single image. The approach presented in this paper aims at training the adapting dictionary from massive samples, other dictionary learning methods such as Online Dictionary Learning (ODL) and Recursive Least Squares Dictionary Learning Algorithm (RLS-DLA) also could train the dictionary by using relative large samples. Our method is competed with the above two state-of-the-art dictionary learning methods. Experiments demonstrate the effectiveness of the proposed dictionary learning in dealing with massive spatial-temporal remote sensing.