Remote Sensing Image Change Detection Combined With Saliency

To improve the accuracy of change detection and solve the issues of missed and false detections in change detection, we conducted research from two perspectives: difference image saliency detection and object-based change vector analysis (CVA). A saliency-guided change detection algorithm was proposed and the CVA method was extended to an object-oriented level. First, we calculated the difference images of the corresponding band for the bitemporal image and then performed principal component analysis (PCA) to extract its first component for convolution, filtering, and other processing to obtain the saliency region. Furthermore, bitemporal images were segmented and split to obtain subobjects, extract various features, such as the spectrum, texture, and shape, and calculate their differences. The main features were optimized using VarSelRF; the initial change region was then obtained using object-based CVA for change detection. The final change region was obtained by combining the assumption that the saliency and intensity of the change region were not uniformly weak with fuzzy rules. This study selected six different complex images for experiments. The results demonstrated that the proposed saliency algorithm for difference images could effectively identify change regions, showing significant advantages compared to classical saliency algorithms. The proposed saliency-guided change detection algorithm achieved a high extraction accuracy, with an overall accuracy and Kappa coefficient typically exceeding 92% and 0.82, respectively. Even in complex environments, the Kappa coefficient reached 0.71. A comparison of the saliency-guided change detection with the pixel-level CVA and ablation experiments using two individual algorithms extraction demonstrated the superiority of the proposed method.