A Copula-Based Method for Change Detection With Multisensor Optical Remote Sensing Images

This article considers the problem of change detection (CD) with multisensor optical remote sensing (RS) images. Copulas are adopted to characterize the dependence structure between the image pair. For this problem, a conditional copula-based CD technique has been proposed in the literature; however, in this technique, it is difficult to select the best copula function in an analytical framework. Resulting copula misspecification may lead to performance degradation. To deal with this problem, we model the CD problem as a binary hypothesis testing problem and propose a new superpixel-level copula-based statistical method (SCOPS) for CD, where an explicit strategy for copula selection is provided for the proposed method. The effectiveness of the copula selection strategy is verified on CD tasks with simulated multisensor optical RS images. Experiments on real RS datasets demonstrate the superiority of SCOPS over state-of-the-art methods.