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 imager...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 Per-ceptron(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 net-work’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.展开更多
基金funded by Comprehensive Remote Sensing for Refined Investigation and Risk Assessment of Geological Hazards in Yunnan Province,grant number YCZH[2020]-68 and The APC was funded by Construction of Yunnan Geological Hazard Identification Center,YCZH[2021]-23 and Fine investigation and risk assessment of geological hazards in key regions of Yunnan Province,YNGH[2021]-168F.Also support was provided by the Chinese Academy of Geological Sciences Basal Research Fund(No.JKYDM2025110)the Open Project Program of Hebei Province Collaborative Innovation Center for Strategic Critical Mineral Research,Hebei GEO University(No.HGUXT-2024-1)+1 种基金the Geological Survey Project of the China Geological Survey(DD20230366)the Open Project of the Technology Innovation Center for Deep Gold Resources Exploration and Mining,Ministry of Natural Resources(No.LDKF-2023BZX-20).
文摘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 Per-ceptron(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 net-work’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.
