A Morphological Model for Extracting Road Networks from High-Resolution Satellite Images

Mohamad M Awad

doi:10.1155/2013/243021

Abstract

Urban planning depends strongly on information extracted from high-resolution satellite images such as buildings and roads features. Nowadays, most of the available extraction techniques and methods are supervised, and they require intensive labor work to clean irrelevant features and to correct shapes and boundaries. In this paper, a new model is implemented to overcome the limitations and to correct the problems of the known and conventional techniques of urban feature extraction specifically road network. The major steps in the model are the enhancement of the image, the segmentation of the enhanced image, the application of the morphological operators, and finally the extraction of the road network. The new model is more accurate position wise and requires less effort and time compared to the traditional supervised and semi-supervised urban extraction methods such as simple edge detection techniques or manual digitization. Experiments conducted on high-resolution satellite images prove the high accuracy and the efficiency of the new model. The positional accuracy of the extracted road features compared to the manual digitized ones, the counted number of detected road segments, and the percentage of completely closed and partially closed curves prove the efficiency and accuracy of the new model.

Highlights

Urban areas are the inhabited areas on earth where most dynamic changes can be observed
Automated Feature Extraction (AFE) methods have been the long-term goal of geospatial data production workflows for the past 30 years; extracted features over small training sets can be applied to larger areas, reducing the extraction time required by several orders of magnitude [4]
Self-Organizing Maps (SOMs)-Fuzzy C-means (FCM) is very efficient compared to some commercial segmentation/classification methods such as ISODATA which stands for Iterative Self-Organizing Data algorithm

Summary

Introduction

Urban areas are the inhabited areas on earth where most dynamic changes can be observed. Benediktsson et al [9] used mathematical morphological operations to extract structural information to detect the urban area boundaries in satellite images. This method is based on neural network architecture. The above research showed success in extracting different urban features, but with limitations due to the complex content and structure of the high-resolution satellite images such as the road-width can vary considerably, presence of lane markings, vehicles, shadows cast by buildings and trees, and changes in surface material. The reader is urged to further investigate the important use of the extracted urban features in the planning and security reasons in many literatures

The New Urban Feature Extraction Model

Experimental Results

Conclusion