PDF(Pubmed)
Abstract：
The extraction of roadways from remote sensing imagery constitutes a pivotal task, with far-reaching implications across diverse domains such as urban planning, management of transportation systems, emergency response initiatives, and environmental monitoring endeavors. Satellite images captured during daytime have customarily served as the primary resource for this extraction process. However, the emergence of Nighttime Light (NTL) remote sensing data introduces an innovative dimension to this arena. The exploration of NTL data for road extraction remains in its nascent stage, and this study seeks to bridge this gap. We present a refined U-Net model (CA U-Net) integrated with Cross-Attention Mechanisms, meticulously designed to extract roads from Yangwang-1 NTL images. This model incorporates several enhancements, thereby improving its proficiency in identifying and delineating road networks. Through extensive experimentation conducted in the urban landscape of Wenzhou City, the model delivers highly accurate results, achieving an F1 score of 84.46%. These outcomes significantly surpass the performance benchmarks set by Support Vector Machines (SVM) and the Optimal Threshold (OT) method. This promising development paves the way towards maximizing the utility of NTL data for comprehensive mapping and analysis of road networks. Furthermore, the findings underscore the potential of utilizing Yangwang-1 data as a reliable source for road extraction and reaffirm the viability of deploying deep learning frameworks for road extraction tasks utilizing NTL data.
摘要：
从遥感图像中提取道路是一项关键的任务，在城市规划等各个领域具有深远的影响，运输系统的管理，应急措施,和环境监测工作。白天捕获的卫星图像通常是此提取过程的主要资源。然而,夜光（NTL）遥感数据的出现为这个领域引入了一个创新的维度。NTL数据用于道路提取的探索仍处于起步阶段，这项研究旨在弥合这一差距。我们提出了一种与交叉注意力机制集成的精细U-Net模型(CAU-Net)，精心设计，从Yangwang-1NTL图像中提取道路。这个模型包含了几个增强功能，从而提高其识别和划定道路网络的熟练程度。通过在温州市城市景观中进行的广泛实验，该模型提供了高度准确的结果，F1得分为84.46%。这些结果大大超过了支持向量机（SVM）和最佳阈值（OT）方法设置的性能基准。这一充满希望的发展为最大化NTL数据用于全面绘制和分析道路网络铺平了道路。此外，这些发现强调了利用Yangwang-1数据作为道路提取的可靠来源的潜力，并重申了利用NTL数据为道路提取任务部署深度学习框架的可行性。
