High Resolution Urban Image Classification Combining Edge Statistical Features

Abstract

Classification with very high resolution (VHR) urban images is challenging because of the great variations of spectrums of pixels inside objects. Plenty of structural information can be obtained over edge statistics. A methodology for incorporating image edge statistical information into conventional classification algorithms is described. The technique is built on the statistical information of edges which are generated by edge statistical model. This method has been tested on a selected site of Worldview-II data which covers north-west part of Beijing, China. Nine land-cover types have been classified to evaluate the effectiveness of edge-based features for urban image classification. The overall classification accuracy is 82.7% and 89.3% for pixel-based and object-based method for incorporating edge statistical features, respectively.