Abstract
A region-based level set segmentation was developed for the automatic detection of man-made objects from aerial and satellite images. The essence of the approach is to optimize the position and the geometric form of an evolving curve, by measuring information within the regions that compose a particular image partition based on their statistical description. The present region-based variational model is fully automated without the need to manually specify the position of the initial contour. Furthermore, it converges after a small number of iterations, allowing real-time applications. The developed algorithm was tested for the detection of roads, buildings and other man-made objects in a number of aerial and satellite images. The effectiveness of the algorithm is demonstrated by the experimental results and the performed qualitative and quantitative evaluation.
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