Building detection in satellite images by textural features and Adaboost

A method based on textural features and Adaboost for detecting buildings in satellite images is proposed. Several local textural features including mean and standard deviation of image intensity and gradient, Zernike moments, Circular-Mellin features, Haralick features, Fourier Power Spectrum, Wavelets, Gabor Filters, and a set features extracted from HSV color space are extracted. Adaboost learning algorithm is employed for both classification and determining the beneficial feature subset, due to its feature selector nature. Some operation including morphological operators are applied for post processing. The approach was tested on a set of satellite images having different types of buildings and promising experimental results are achieved.