Coupled Dictionary Learning for Change Detection From Multisource Data

With the increase of multisource data available from remote sensing platforms, it is demanding to develop unsupervised techniques for change detection from multisource data. The difference in imaging mechanism makes it difficult to carry out a direct comparison between multisource data in original observation spaces. Different sensors provide different descriptions on the same truth in low-dimension observation spaces, but the same truth indicates the comparability of multisource data in some high-dimensional feature spaces. Inspired by this, we try to solve this problem by transforming multisource data into a common high-dimension feature space. In this paper, an iterative coupled dictionary learning (CDL) model is proposed for multisource image change detection. This model aims to establish a pair of coupled dictionaries, one of which is responsible for the data from one sensor, whereas the other is responsible for the data from another sensor. The atoms from these two coupled dictionaries have a one-to-one correspondence at the same location. Such a property guarantees the transferability of the reconstruction coefficients between bitemporal patch pairs and provides us a desired mechanism to bridge multisource data and highlight changes. The contributions can be summarized as follows: CDL is designed to explore the intrinsic difference of multisource data for change detection in a high-dimension feature space, and an iterative scheme for unsupervised sample selection is proposed to keep the purity of training samples and gradually optimize the current coupled dictionaries. The experimental results have demonstrated the feasibility, effectiveness, and robustness of the proposed framework.