Zernike moments and svm for shape classification in very high resolution satellite images

In this paper, a Zernike moments-based descriptor is used as a measure of shape information for the detection of buildings from Very High Spatial Resolution (VHSR) satellite images. The proposed approach comprises three steps. First, the image is segmented into homogeneous objects based on the spectral and spatial information. MeanShift segmentation method is used for this end. Second, a Zernike feature vector is computed for each segment. Finally, a Support Vector Machines (SVM)-based classification using the feature vectors as inputs is performed. Experimental results and comparison with Environment for Visualizing Images (ENVI) commercial package confirm the effectiveness of the proposed approach.