Road extraction from aerial and satellite images by dynamic programming

Abstract

In this paper, we propose a semi-automatic road extraction scheme which combines the wavelet decomposition for road sharpening and a model-driven linear feature extraction algorithm based on dynamic programming. Semi-automatic means that a road is extracted automatically after some seed points have been given coarsely by the operator through activation of a mouse using a convenient interactive image-graphics user interface. With a wavelet transform interesting image structures can be enhanced and a multiresolution representation can be obtained by selection of a special wavelet. We have built a special wavelet for road sharpening, which has been implemented as a fast pyramidal algorithm. In the model-driven feature extraction scheme, a road is represented by a generic road model with six photometric and geometric properties. This model is formulated by some constraints and a merit function which embodies a notion of the “best road segment”, and evaluated by a “time-delayed” dynamic programming algorithm. The mathematical foundation and issues relating to its practical implementation are discussed in detail. This approach has been applied very successfully to extract complete road networks from single SPOT scenes and aerial images. Thereby the algorithm runs in a monoplotting mode, deriving X, Y, Z-coordinates of the roads, whereby the Z-component comes from real-time interpolation within an underlying DTM. Some experimental results are also given in this paper.