An Unsupervised Change Detection Approach for Remote Sensing Image Using Visual Attention Mechanism.

In this paper, we propose a novel approach for unsupervised change detection by integrating visual attention mechanism which has the ability to find the real changes between two images. The approach starts by generating a difference image using the differential method. Subsequently, an entropy-based saliency map is generated in order to highlight the changed regions which are regarded as salient regions. Thirdly, a fusion image is generated using difference image and entropy-based saliency map. Finally, the K-means clustering algorithm is used to segment the fusion image into changed and unchanged classes. To demonstrate the effect of our approach, we compare it with the other four state-of-the-art change detection methods over two datasets, meanwhile extensive quantitative and qualitative analysis of the change detection results confirms the effectiveness of the proposed approach, showing its capability to consistently produce promising results on all the datasets without any priori assumptions.