A saliency-guided neighbourhood ratio model for automatic change detection of SAR images

In this paper, a new approach to the saliency-guided neighbourhood ratio method has been proposed to change-detection of Synthetic Aperture Radar (SAR) images. Two methods, the saliency-guided and the neighbourhood ratio generate a vector as a feature to classify the image difference of SAR images into changed and unchanged classes by using a clustering method such as k-means clustering. Therefore, in this study, the vectors obtained from the saliency-based neighbourhood ratio method was used as separate properties in the k-means clustering algorithm to classify the area into two classes, changed and unchanged region. Furthermore, the saliency-guided method uses a threshold value to generate a saliency map that is selected by trial and error for each data set. In this study, the Otsu threshold histogram method was used to calculate the threshold, which leads to higher accuracy of the method and the automatic selection of the threshold at a higher rate. Moreover, in the neighbourhood ratio model, the probability distribution function image was used instead of the average ratio image. Next, the proposed method has been implemented in three SAR data sets of Bern, Ottawa, and Yellow River China as ground truth data to evaluate the accuracy of the model. The results of the proposed model show that the kappa coefficient (kappa) of the Berne data set is increased from 0.8705 in the saliency-guided model and 0.8590 in the neighbourhood ratio method to 0.8821. Also, for the Ottawa data set, the kappa of the proposed model has been raised by 0.1 and 0.3190 compared to the saliency-guided and neighbourhood ratio methods, respectively. Finally, the accuracy of the proposed model in the Yellow River data set, respectively, 0.45 and 0.57, is improved than saliency-guided and neighbourhood ratio models.