An Integrated Multistage Framework for Automatic Road Extraction from High Resolution Satellite Imagery

Abstract

Automated procedures to rapidly identify road networks from high-resolution satellite imagery are necessary for modern applications in GIS. In this paper, we propose an approach for automatic road extraction by integrating a set of appropriate modules in a unified framework, to solve this complex problem. The two main properties of roads used are:(1) spectral contrast with respect to background and (2) locally linear path. Support Vector Machine is used to discriminate between road and non-road segments. We propose a Dominant singular Measure (DSM) for the task of detecting linear (locally) road boundaries. This pair of information of road segments, obtained using Probabilistic SVM (PSVM) and DSM, is integrated using a modified Constraint Satisfaction Neural Network. Results of this integration are not satisfactory due to occlusion of roads, variation of road material, and curvilinear pattern. Suitable post-processing modules (segment linking and region part segmentation) have been designed to address these issues. The proposed non-model based approach is verified with extensive experimentations and performance compared with two state-of-the-art techniques and a GIS based tool, using multi-spectral satellite images. The proposed methodology is robust and shows superior performance (completeness and correctness are used as measures) in automating the process of road network extraction.

Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images

“…Generally, state-of-the-art methods for road-feature extraction from VHR images fall into two categories: Automatic and semiautomatic methods. Automatic approaches require no prior information and can be executed by a series of image-processing algorithms, such as mathematical morphology [11,12], active snake model [13], dynamic programming [14], neural networks [15][16][17], probabilistic graphical models [18], filtering-based methods [19], and object-oriented methods [20]. In general, however, the unsatisfactory performance of the automatic method in road-feature extraction from images presenting complex natural road scenarios (e.g., image noise and tree and shadow occlusion) restricts its practical applications [21].…”