A kernel matching pursuit approach to man-made objects detection in aerial images

Abstract

This paper describes a new aerial images segmentation algorithm. Kernel Matching Pursuit (KMP) method is introduced to deal with the nonlinear distribution of the man-made objects' features in the aerial images. In KMP algorithm, a lot of training samples containing substantive information are used to detect the man-made objects. With KMP classifier, pixels in large aerial images will be labeled as different prediction values, which can be classified linearly. Then the modified Mumford-Shah model, which comprises the features of the KMP prediction values, is built to segment the aerial image by necessary level set evolution. The proposed method is proven to be effective by the results of experiments. © Springer-Verlag Berlin Heidelberg 2007.