# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## 1 Introduction
These studies demonstrated the valuable role of high-resolution optical imagery in monitoring open-pit mines changes. While optical imagery provides essential information about surface features and land cover, its application is not always possible in areas with frequent cloud cover or during adverse weather conditions. In contrast, synthetic aperture radar (SAR) imaging, with its cloud-penetrating capabilities, has emerged as a promising alternative, revolutionizing the way changes are observed and analyzed. SAR images provide detailed information about the physical properties of the terrain, allowing for precise identification and monitoring of changes that may go unnoticed by other sensors. However, compared to the optical community, the SAR community faces a lack of medium-sized change detection datasets. While datasets such as Ottawa, Bern, San Francisco, Yellow River--Farmland, C., Yellow River--Farmland, D., and Shimen [13; 14; 15; 16; 17] have been commonly used in SAR change detection research, their sizes are not comparable to those of optical datasets [18; 19; 20; 21; 22]. Consequently, training large supervised deep learning models using these datasets becomes challenging. The scarcity of manually labeled SAR images can be attributed to the complexities involved in their visual interpretation, particularly for inexperienced users. Factors like multiplicative speckle interference, geometric distortions, and the grayscale nature of SAR data make interpreting SAR images challenging. As a result, the SAR community has primarily focused on unsupervised methods of change detection. These methods typically involve four steps: preprocessing, difference image generation, feature extraction, and classification. Preprocessing aims to reduce speckle noise through denoising techniques [23; 24; 25]. The difference image generation step applies a difference operator (e.g., log-ratio [15], Gauss-ratio [16], neighborhood-based ratio [17]) to the two acquisitions, generating a map that serves as the basis for feature extraction. Clustering approaches such as K-means, fuzzy C-means, and hierarchical clustering are commonly used to distinguish change and no-change pixels based on the extracted features. While these methods can effectively identify simple changes, they may not be suitable for more complex change scenarios. The limitations faced by SAR imagery have underscored the need for advanced techniques to improve SAR-based change detection. Recently, some works on supervised deep learning models have been presented. [PERSON] [26] proposed a dual-domain network that leverages both spatial and frequency domain features by integrating reshaped DCT coefficients into the model's frequency domain branch. [PERSON] [27] proposed a deformable residual convolutional neural network (DRNet), which address the limitations of fixed sampling locations in traditional CNNs and the need for stronger multi-scale representation. [PERSON] [28] presented a generalized Gamma deep belief network to address SAR change detection. By extracting hierarchical features and fitting the distribution of difference images, the model learns joint high-level representations for accurate change mapping. [PERSON] [29] presented a SAR image change detection algorithm that combines saliency detection and convolutional-wavelet neural networks. [PERSON] [30] proposed a fully convolutional network with a skip connection to identify newly constructed buildings. [PERSON] [31] introduced a change detection method for sea ice using convolutional-wavelet neural networks (CWNNs). The approach incorporates the dual-tree complex wavelet transform into a CWNN for accurate classification of changed and unchanged pixels. [PERSON] [32] proposed CD-TransUNet for changing building detection in SAR images. CD-TransUNet combines UNet and transformer architectures and incorporates techniques such as coordinate attention, atrous spatial pyramid pooling, and depthwise separable convolution to enhance feature extraction precision and reduce computational complexity.