Enhancing fault detection using CHRRA-Unet and focal loss functions for imbalanced data：A case study in Luoping county, Yunnan, China
点击次数：
所属单位：
中南大学地球科学与信息物理学院
教研室：
GIS
发表刊物：
Artificial Intelligence in Geosciences
刊物所在地：
中国
关键字：
Fault detection
Imbalance datasets
Focal loss function
CHRRA-Unet
Hrrformer models
Remote sensing data
摘要：
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 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.
第一作者：
Gong Cheng
论文类型：
期刊论文
通讯作者：
Yingdong Yang, Syed Hussain
论文编号：
100163
学科门类：
工学
一级学科：
地质资源与地质工程
文献类型：
J
卷号：
7
是否译文：
否
收录刊物：
SCI
附件：
学位：博士学位
学科：地质资源与地质工程. 地质学. 测绘科学与技术
所在单位：地球科学与信息物理学院