Aerial image classification using structural texture similarity

There is an increasing need for algorithms for automatic analysis of remote sensing images and in this paper we address the problem of semantic classification of aerial images. For the task at hand we propose and evaluate local structural texture descriptor and similarity measure. Nearest neighbor classifier based on the proposed descriptor and similarity measure, as well as image-to-class similarity, improves classification rates over the state-of-the-art on two datasets of aerial images. We evaluate the design choices and show that rich subband statistics, perceptually-based structural texture similarity measure and image-to-class similarity all contribute to the good performance of our classifier.