In this work, an integrated framework comprising of pixel-based classification, road network filtering, and multi-scale Gabor filter is proposed to address the various prevailing issues in road centerline extraction from VHR images. The proposed framework is composed of three steps; generation of the initial road map, road network filtering and road centerline extraction. In the first step, pixel-based classification using support vector machine (SVM) is performed on VHR imagery to classify it into the road and non-road classes. In the road network filtering step, to retain the road features in classified imagery, an edge-preserving guided filter is applied and to improve the veracity of road extraction, undesirable components are eliminated by shape feature analysis. Finally, the complete and accurate road centerline are obtained by combining multi-scale Gabor filter and fast parallel thinning algorithm. The maximum response from a multi-scale Gabor filter is not only used for extraction but also used for reconstructing the broken road network, which is due to occlusions and artifacts. The proposed framework is implemented on publically available VHR images dataset. The simulation outcomes reflect the dominance of the proposed framework on different quantitative evaluation parameters as compared to up-to-date methods.

在这项工作中，提出了一个包含基于像素的分类，道路网络滤波和多尺度Gabor滤波器的集成框架，以解决从VHR图像提取道路中心线时出现的各种主要问题。拟议的框架包括三个步骤：生成初始路线图，路网过滤和道路中心线提取。第一步，在VHR图像上使用支持向量机（SVM）进行基于像素的分类，以将其分类为道路和非道路类别。在路网过滤步骤中，为了将道路特征保留在分类图像中，应用了边缘保留导向过滤器，并且为了提高道路提取的准确性，通过形状特征分析消除了不需要的成分。最后，结合多尺度Gabor滤波器和快速并行稀疏算法，可获得完整，准确的道路中心线。来自多尺度Gabor滤波器的最大响应不仅用于提取，而且还用于重建断路网，这是由于遮挡和伪影所致。所提出的框架是在可公开获得的VHR图像数据集上实现的。与最新方法相比，仿真结果反映了所提出框架在不同定量评估参数上的优势。所提出的框架是在可公开获得的VHR图像数据集上实现的。与最新方法相比，仿真结果反映了所提出框架在不同定量评估参数上的优势。所提出的框架是在可公开获得的VHR图像数据集上实现的。与最新方法相比，仿真结果反映了所提出框架在不同定量评估参数上的优势。