A two-step approach for surface type classification of aerial images

In this paper, we propose a two-step method for surface type classification of aerial images. Aerial image segmentation is the first step of this approach, and defining the surface type of each segment is performed in the next step. In this method, firstly 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, different kinds of color and texture features have been tested and resulted in choosing Gabor filters in HSV color space for the best segmentation result. After aerial images segmentation, surface type of individual areas is classified to an accuracy of about 97% using k-nearest neighbor (KNN) algorithm and the same color and texture features.