An Integrated Multistage Framework for Automatic Road Extraction from High Resolution Satellite Imagery |
| |
| Authors: | T T Mirnalinee Sukhendu Das Koshy Varghese |
| |
| Institution: | (1) Visualization and Perception Lab, Dept. of CSE, Indian Institute of Technology, Madras, Chennai, 600 036, India;(2) Dept. of Civil Engg, Indian Institute of Technology, Madras, Chennai, 600 036, India |
| |
| 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. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
|
