Morphological attribute profiles (APs) obtained by the sequential application of morphological attribute filters to images have been found very effective in remote sensing (RS) to characterize spatial properties of objects in a scene. However, a direct use of the APs can be insufficient to provide a complete characterization of spatial information when complex texture is present in the considered images. To overcome this problem, in this paper, we present the novel histogram-based morphological APs (HAPs). The HAPs model the marginal local distribution of attribute filter responses to better characterize the texture information, and they are obtained based on a three-step algorithm. In the first step, the standard APs are constructed by sequentially applying attribute filters to the considered image. In the second step, a local histogram is calculated for each sample of each image in the APs. Then, in the final step, the local histograms of the same pixel locations in the APs are stacked, resulting in a texture descriptor whose components represent local distributions of the filter responses for the related pattern. Finally, the very-high-dimensional HAPs are classified by a support vector machine (SVM) classifier with histogram intersection kernel. Experimental results obtained by considering two very high resolution panchromatic images show the effectiveness of the proposed HAPs, which sharply improve the accuracy of the SVM classifier with respect to standard AP-based methods.
['Begum Demir', 'Lorenzo Bruzzone']
Histogram-Based Attribute Profiles for Classification of Very High Resolution Remote Sensing Images