Unsupervised change detection using spectral features and a texture difference measure for VHR remote-sensing images

Abstract

This article proposes an unsupervised change-detection method using spectral and texture information for very-high-resolution (VHR) remote-sensing images. First, a new local-similarity-based texture difference measure (LSTDM) is defined using a grey-level co-occurrence matrix. A mathematical analysis shows that LSTDM is robust with respect to noise and spectral similarity. Second, the difference image is generated by integrating the spectral and texture features. Then, the unsupervised change-detection problem in VHR remote-sensing images is formulated as minimizing an energy function related with changed and unchanged classes in the difference image. A modified expectation-maximization-based active contour model (EMCVM) is applied to the difference image to separate the changed and unchanged regions. Finally, two different experiments are performed with SPOT-5 images and compared with state-of-the-art unsupervised change-detection methods to evaluate the effectiveness of the proposed method. The results indicate that the proposed method can sufficiently increase the robustness with respect to noise and spectral similarity and obtain the highest accuracy among the methods addressed in this article.