Enhancing Fault Detection Using CHRRA-Unet and Focal Loss Functions for Imbalanced Data: A Case Study in Luoping County, Yunnan, China

Abstract

Recent advancements in remote sensing technology have made it easier to detect surface faults. Deep learning, especially convolutional models, offers new potential for automatic fault detection from remote sensing imagery. However, these models often struggle with segmentation accuracy due to their limitations in handling spatial hierarchies and short-range dependencies. They process data in local contexts, which is insufficient for tasks requiring an understanding of global structures, like fault detection. This leads to inaccurate boundary divisions and incomplete fault trace detections. To address these issues, the Convolution Holographic Reduced Representations-Based Unet (CHRRA-Unet) is introduced. This U-shaped network combines convolution and a novel attention-based transformer for remote sensing image segmentation. By extracting both local and global features, the CHRRA-Unet significantly improves the detection of geological faults in remote sensing images. By incorporating a convolutional module (CM) and holographic reduced representation attention (HRRA), local and global feature extraction is improved. To minimize computational complexity, the traditional Multi-Layer Perceptron (MLP) is replaced with the Local Perception ModuleRecent advancements in remote sensing technology have made it easier to detect surface faults. Deep learning, especially convolutional models, offers new potential for automatic fault detection from remote sensing imagery. However, these models often struggle with segmentation accuracy due to their limitations in handling spatial hierarchies and short-range dependencies. They process data in local contexts, which is insufficient for tasks requiring an understanding of global structures, like fault detection. This leads to inaccurate boundary divisions and incomplete fault trace detections. To address these issues, the Convolution Holographic Reduced Representations-Based Unet (CHRRA-Unet) is introduced. This U-shaped network combines convolution and a novel attention-based transformer for remote sensing image segmentation. By extracting both local and global features, the CHRRA-Unet significantly improves the detection of geological faults in remote sensing images. By incorporating a convolutional module (CM) and holographic reduced representation attention (HRRA), local and global feature extraction is improved. To minimize computational complexity, the traditional Multi-Layer Perceptron (MLP) is replaced with the Local Perception Module (LPM). The Multi-Feature Conversion Module (MFCM) ensures an effective combination of feature maps during encoding and decoding, enhancing the network's ability to accurately detect fault traces. Extensive experiments show that CHRRA-Unet achieves a high accuracy rate of 97.20% in remote sensing image segmentation, outperforming existing models and providing superior fault detection capabilities over current methods. (LPM). The Multi-Feature Conversion Module (MFCM) ensures an effective combination of feature maps during encoding and decoding, enhancing the network's ability to accurately detect fault traces. Extensive experiments show that CHRRA-Unet achieves a high accuracy rate of 97.20% in remote sensing image segmentation, outperforming existing models and providing superior fault detection capabilities over current methods.

Keywords: Fault detection, Imbalance datasets, Focal loss Function, CHRRA-Unet, Remote sensing data