Road Extraction for Satellite Imagery
Unsupervised extraction of roads eliminates the need for human operators to perform the time consuming and expensive process of mapping roads from satellite imagery. As increasing volumes of imagery become available, fully automatic methods are required to interpret the visible features such as roads, railroads, drainage, and other meaningful curvilinear structures in multi-spectral satellite imagery. There exists an even greater need for a mechanism that handles low-resolution images. The essence of detecting curvilinear elements is also related to the problem of deriving anatomical structures in medical imaging as well as locating material defects in product quality control systems, geomorphologic and cartographic applications.
Background & Objective: Most of the proposed road detection algorithms require user assistance to mark both starting and ending points of road segments. Due to the noise sensitivity, asymmetry of the contrast at the both sides of the edges, and the difficulty of obtaining precise edge directions, edge based methods are inadequate for very low-resolution multi-spectral imagery. Our main objective is to develop automatic, robust, and computationally feasible road detection and satellite imagery analysis algorithms.
Technical Discussion: First, the input image is filtered to suppress the regions that the likelihood of existing a road pixel is low. Then, the road magnitude and orientation are computed by evaluating the responses from a quadruple orthogonal line filter set. A mapping from the line domain to the vector domain is used to determine the line strength and orientation for each point. A major problem of road extraction algorithms is disconnected road segments due to the poor visibility of the roads in the original image. Often roads are divided into several short segments, or completely missing from the image. To solve this problem, we fit Gaussian models to image points, which represent the likelihood of being road points. These models are evaluated recursively to determine the correlation between the neighboring points. The iterative process consists of finding the connected road points, fusing them with the previous image, passing them through the directional line filter set and computing new magnitudes and orientations. The road segments are updated, and the process continues until there are no further changes in the roads extracted.
Technology Area: Computer Vision
Modification Date: July 15, 2004