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AUTOMATIC LINEAR FEATURE EXTRACTION OF IRANIAN ROADS FROM HIGH RESOLUTION MULTI-SPECTRAL SATELLITE IMAGERY

Attaining geospatial information is a challenge for many scientific practitioners. Such information is a necessary tool for spatial decision making. Remote Sensing (RS) is the leading art/science providing the data for many global or local applications such as: green house effect, pollution, military, urban and land use. Graphical elements of geospatial information can be divided into: points, lines, and planimetric features. The most prominent linear topographic features are roads, rivers, railways and vegetation boundaries. Roads are important large-network man-made structures. All the elements can be derived from RS images. Many efforts have been performed to extract proper information efficiently. They can be classified into: manual and automatic feature extraction. Manual techniques are fading away as they are inefficient and inaccurate. Automatic extraction of geospatial phenomena has been the subject of extensive research for the past decade. Feature extraction approaches are diverse especially for linear features whose major methodologies are: fusion-based, fuzzy-based, mathematical morphology, model-based approach, dynamic programming and multiscale grouping. In this paper, an approach based on fuzzy and mathematical morphology is introduced. In the developed fuzzy process, each pixel is transformed into a matrix of membership degrees representing the fuzzy inputs. A minimum-reasoning rule is, then, applied to infer the fuzzy outputs. Finally, a defuzzification step is applied to extract features. Advanced morphological concepts: ‘trivial opening’, ‘granulometry’ and ‘skeleton’ are applied to remove small objects, narrow paths and noises automatically. In addition, shadows of trees and buildings that cause partially covered roads are recovered. This object of this paper is to illustrate an applied method for automatic extraction of Iranian roads from pan-sharpened IKONOS images. The method is successfully executed on different regions including urban, suburban and rural areas. It is concluded that extracted road centrelines are so accurate and precise. The results are more promising at the crosses and curved segments. The extracted road centreline is easily inserted in a GIS. For future work, authors intend to introduce the fuzzy classification method into an artificial neural network program. In addition, the illustrated method can be used for the purpose of change detection in the road network system of a city from high resolution satellite images.