Histogram based attribute profiles for classification of very high resolution remote sensing images

This paper presents a novel histogram based attribute profiles (HAPs) technique for classification of very high resolution remote sensing images. The HAPs characterize the marginal local distribution of attribute filter responses to model the texture information. This is achieved based on a two steps algorithm. In the first step the standard attribute profiles (AP) are built through sequential application of attribute filters to the considered image. In the second step a local histogram is initially computed for each sample of each image in the APs. Then the local histograms of the same pixel locations in the APs are concatenated. Accordingly, each sample is characterized by a texture descriptor whose components model local distributions of the filter responses. Finally the very high dimensional HAPs are classified by a Support Vector Machine classifier with histogram intersection kernel, which is very effective for high dimensional histogram-based feature representations. Experimental results confirm the effectiveness of the proposed HAPs with respect to standard APs.