Automatic aerial image segmentation based on a modified Chan-Vese algorithm

Automatic segmentation of aerial images has been a challenging task in recent years. Region-based active contour of Chan-Vese has been proposed to detect objects in a given image. This algorithm is more powerful than classical edge-based active contour algorithms. In this paper, aerial images are automatically segmented into a number of homogeneous areas using Chan-Vese model implemented by Narrow Band Level Set method with reinitialization together with extracting color and texture features. For this purpose, a variety of different color and texture features have been tested. The results show that incorporation of Gabor filters in HSV color space leads the most accurate results.