Research on the Extraction of Hazard Sources along High-Speed Railways from High-Resolution Remote Sensing Images Based on TE-ResUNet

Abstract

There are many potential hazard sources along high-speed railways that threaten the safety of railway operation. Traditional ground search methods are failing to meet the needs of safe and efficient investigation. In order to accurately and efficiently locate hazard sources along the high-speed railway, this paper proposes a texture-enhanced ResUNet (TE-ResUNet) model for railway hazard sources extraction from high-resolution remote sensing images. According to the characteristics of hazard sources in remote sensing images, TE-ResUNet adopts texture enhancement modules to enhance the texture details of low-level features, and thus improve the extraction accuracy of boundaries and small targets. In addition, a multi-scale Lovász loss function is proposed to deal with the class imbalance problem and force the texture enhancement modules to learn better parameters. The proposed method is compared with the existing methods, namely, FCN8s, PSPNet, DeepLabv3, and AEUNet. The experimental results on the GF-2 railway hazard source dataset show that the TE-ResUNet is superior in terms of overall accuracy, F1-score, and recall. This indicates that the proposed TE-ResUNet can achieve accurate and effective hazard sources extraction, while ensuring high recall for small-area targets.

Color-Coated Steel Sheet Roof Building Extraction from External Environment of High-Speed Rail Based on High-Resolution Remote Sensing Images

“…To improve the CCSS roof building extraction accuracy, Pan et al [13] proposed a texture-enhanced ResUNet that applied histogram quantization to enhance boundary details. However, performing simple grayscale enhancement solely within the spatial dimension is insufficient to address issues stemming from intricate backgrounds and irregular shapes.…”

“…These objects pose safety hazards around railways. Common categories of safety hazard sources include color-coated steel sheet (CCSS) roof buildings, plastic greenhouses, and dust-proof nets [13].…”

Gradient Structure Information-Guided Attention Generative Adversarial Networks for Remote Sensing Image Generation

“…The gradient block is used to obtain the gradient map of the original image of the sample without considering the gradient direction information but using a convolutional layer with a fixed kernel, as shown in Equation (1). The gradient calculation formula used is shown in Equation (2).…”

“…In recent years, aerospace remote sensing imaging technology has been applied to many fields, such as land and mineral resource management and monitoring, traffic and road network safety monitoring, geological disaster early warning, and national defense system construction [1][2][3]. Meanwhile, deep learning technology has also greatly promoted the research of remote sensing images in detection and classification [4].…”