Road Network Detection by Mathematical Morphology
Shunji Murai, Chunsun Zhang
Space Technology Applications and Research Program
Asian Institute of Technology
Klong Lunag, Pathumthani 12120, Thailand
E-mail: murai@ait.ac.th , chunsun@ait.ac.th
Keywords: Road Detection, Mathematical Morphology, Trivial Opening, Granulometry.
Abstract
An approach to achieve automated road network detection from high resolution digital image by mathematical morphology operation is presented. The approach proposed in this paper firstly classifies image to find road network region, then morphological trivial opening is adopted to avoid noises. The developed method has been tested on the simulated image with 1meter resolution. The result shows that mathematical morphological provides an effective tool for automated road network detection.
1. Introduction
The extraction of road from digital image has drawn considerable attention in the past years. The strategies fall into two broad categories. In semi-automatic schemes, an operator selects a few mark points of a road segment and then an algorithm based on dynamic programming or least square B-spline active contour model finds the roads (Gruen and Li, 1994, 1997, 1997). Other semi-automatic approach are based on road tracking (Makeown et al. 1988, Vosselman et al. 1995) that start from a given point and a given direction after extracting parallel edges or by extrapolating and matching of profiles. These semi-automatic approaches can be extended to fully automatic operation by means of automatic seed point detection (Zlotnic et al. 1993, Baumgarter et al. 1997, Mayer et al. 1997). Other fully automatic approaches are based on line extraction methods (Wang et al. 1992, Heipke 1995, Gong and Wang 1996) or knowledge-based methods (Stilla et al. 1994, Rusknone 1996, Marlies et al. 1996, Trinder et al. 1997).
Usually line extrapolation method works reasonably well on the low resolution image. However, there can be many other features with properties similar to road which will be extracted as well. Knowledge-base method involves the use of GIS and rules, but there are still a lot of problem to be solved to obtained satisfactory result from remote sensing data as digital images.
Several road models were developed by researchers. The road appearance in imagery depends on sensor sensitivity and its resolution. The authors' approach will restrict to high resolution gray scale image with 1meter resolution. A road in high resolution image is light continuous and homogeneous region so that a good contrast to its adjacent area. One road usually has a constant width, and road at different level has different width, roads from a network.
The objective of this study is to develop an algorithm for automated road network detection from 1meter high resolution image. After road is segmented from background Morphological trivial opening has been developed for the purposes: (!) to perform granulometry analysis and obtain size information of road network, and (2) to extract road network from preprocessed image and differentiate other feature with similar properties of road.
2. Morphological Trivial Opening and Granulometry
Mathematical morphology is a set theory approach, developed by Serra (1982). Based on a formal mathematical framework, mathematical morphology provides an approach to the processing of digital images that is based on geometrical shape.
2.1 Morphological Trivial Opening
Trivial opening (denoted hereafter TO) is defined by Serra and Vincent (1992). Let X be an image, {X(n)\n =1, 2, 3, …..N] be a series of connected components in the image, x(i0 be a point in X(i), we define the trivial opening with a criterion T, as follow
Trivial opening provides a practical mean for object detection and identification. It does not affect the shape and size of the connected regions that are preserved because it preserves the entire connected regions. Since a road in high resolution image is appeared as a narrow homogeneous area forming whole network, the criterion can be selected as the long axes of minimum ellipse which encloses an object. Trivial opening for road detection is expressed as TO_ROAD_DETECTION = { X | Long axis of minimum ellipse enclosing X(i) >= T}
The connected components are reached by morphological reconstruction. Suppose one pixel Y in Xi is searched, then reconstruction Xi from Y is obtained by iterating elementary geodesic dilation of Y inside Xi until stability.
