Land cover classification of multispectral satellite images using QDA classifier

This paper presents a scheme for the classification of multispectral satellite images into multiple predefined land cover classes. The proposed approach results in a fully automatic classification model to assign each pixel in the image to a group of pixels based on reflectance or spectral similarity where each subset of group of pixels is called ground-truth data. The input image is preprocessed and applied to a classifier. The proposed supervised classifier incorporates both spectral and spatial information. We implement QDA Classifier (Quadratic Discriminant Analysis) based on spectral features. The classified image is then post processed using Probabilistic Label Relaxation algorithm for smoothening the output image which gives better results. The QDA Classifier uses statistical classification to separate measurements of two or more classes of objects or events by a quadric surface. It estimates the probability of each class across the spectral domain that it takes into account the correlations of the data set from the class centroid. An experiment on multispectral satellite images shows the accuracy of the method.