# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 1 Introduction
This method involves obtaining a complete set of pixels in a multi-temporal image, and calculating the average pixel probability, to enable the refinement and classification of boundary information through the regional potential function. Gong et al. [52] proposed the patch matching method for fully connected CRF optimization, which combined with the results of semantic segmentation network to detect architectural changes in dual-temporal images. However, the post-processing method based on the front-end output still causes the loss of change-detection information. To address this limitation, Zheng et al. [53] proposed a new end-to-end deep twin CRFs network (PPNet) for high-resolution remote sensing images. The detection results obtained by PPNet are able to refine the edges of change regions and effectively eliminate noise. Overall, deep learning has shown promising results in change detection of the remote sensing image, and the proposed models and techniques have significantly improved the accuracy and efficiency of the process. Although the change-detection methods for remote sensing imagery have made significant progress, there are still challenges in detecting the direction of areas of change by analyzing difference images, which can be summarized as follows. Firstly, difference images may cause a semantic collapse phenomenon. The original temporal image, as shown in Figure 1a,b, contains obvious feature classification information, i.e., semantic information. However, the difference operation, as shown in Figure 1c,d, can quickly locate the change region, but it also leads to a typical semantic collapse phenomenon where the semantic classification information disappears. Figure 1: Plot of the difference of Berne data processed by different methods. (**a**) Multitemporal image 1. (**b**) Multitemporal image 2. (**c**) Log-ratio difference image. (**d**) Mean-ratio difference image. Secondly, the multimodal difference images provide complementary information. As demonstrated in the two modal difference images of the Berne data in Figure 1c,d, the log-ratio difference image has less interference, but the change information is weak, which results in serious missed alarms. On the other hand, the mean-ratio difference image has prominent change information, but the strong interference leads to high false alarms. Thus, the interference and change performance of the two modal difference images are quite different, and improving the semantic perception of change detection through the complementary information of modal difference images is key to improving the overall performance of change detection. To address the challenges mentioned above, this paper proposes a hierarchical fusion SAR image change-detection method based on hierarchical fusion conditional random field (HF-CRF). The main contributions are as follows. * Designing a dynamic region convolutional semantic segmentation module with a dual encoder-single decoder structure (D-DRUNet). It involves constructing a unary potential function by fusing multimodal difference-image features using neural networks, and enhancing the semantic perception capability of the CRF model. * Introducing a boundary prior to constructing a pair-wise potential function based on multimodal dynamic fusion, and enhancing the boundary perception capability of the CRF model. This paper consists of the following three parts: the Method section provides a detailed description of the principle and implementation steps of the proposed method; the Experiment section provides the experimental results and analysis; and the Conclusion section summarizes the article.