# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 1 Introduction  
Change detection using remote sensing technology is a valuable research technique in the field of Earth observation [1, 2, 3]. It quantitatively analyzes multi-temporal images of the same geographical area to determine surface change characteristics [4]. The needs and standards in the field of communication are increasing [5, 6] in response to the expanding range of human trajectories. The proposed integrated space-air-ground network has the potential to greatly enhance the efficiency of acquiring various types of information data and improve the computational efficiency of such data [7, 8]. Incorporating edge computing tasks can improve the overall availability and scalability of the system [9, 10, 11]. By combining edge computing technology with remote sensing and satellite communication networks, the quality of satellite communication can be improved and the processing capability of satellite tasks can be enhanced, all while ensuring efficient resource scheduling [12, 13]. Overall, the proposed integrated space-air-ground network has the potential to greatly influence the efficiency of acquiring various types of information data as well as the computational efficiency of such data. The rich remote sensing data acquired by airborne remote sensing satellites can be used to describe urban land use, cover types and their detailed changes over time [2, 14, 15]. The field of remote sensing image change detection mostly utilizes a single optical image data source as the research target [16]. However, the imaging quality of optical remote sensing images is highly susceptible to complex weather, as well as satellite performance. Synthetic-aperture radar (SAR) images contain richer pixel information and clearer detailed information, which can effectively improve the limitations of optical image-based methods in the field of remote sensing change detection. SAR is an active microwave remote sensing technique [17; 18] that could operate in less restricted natural environments [19] and plays an important role in remote sensing. The remote sensing change-detection method based on SAR images demonstrates significant advantages in the integrated space-air-ground tasks, such as urban building change detection [20], forest fire location [21], and geological disaster monitoring [22; 23; 24]. Currently, the predominant techniques of image change detection are based on difference-image detection [25], which involves analyzing the difference images generated from simultaneous phase images to obtain the final binary change maps [26]. Recently, deep learning has been applied to remote sensing image change detection, with the aim of learning complex features by constructing multilayer network models and training on huge amounts of data [27; 28; 29; 30]. How to handle deep-level change information [31] is one of the key challenges in applying deep learning to change detection in the field of remote sensing imagery. Taking advantage of the powerful feature-capture capability of the convolutional neural network (CNN) [32; 33; 34], Chen [28] proposed DSMS-CN and DSMSFCN methods for change detection of multi-temporal high-resolution remote sensing images based on multi-scale feature convolution units. Wu J et al. [29] proposed a deep supervised network (DSAHRNet) model. After the network extracts the decoding change information, the features are refined by parallel stacking of convolutional blocks, and more discriminative features can be obtained with the deep supervised module. The attention mechanism is introduced in change-detection convolutional networks to focus on change information in complex information as well. Chen, J et al. [30] proposed DASNet, a change-detection model based on the dual attention full convolutional twin neural network, to obtain change-detection results by extracting rich features from dual temporal phase maps. Li et al.