Settlement type classification using aerial images.

Abstract

In metropolitan and urban areas, the problems relating to rapid transformations that are taking place in terms of land cover and land use are now very pronounced, e.g., the rapid increase and unpredictable spread of formal and informal physical infrastructure. As a result, the availability of detailed, timely information on urban areas is of considerable importance both to the management of current urban activities and to forward planning. Remote sensing sources can make a vital contribution in this context, since they provide regular and recurring data from a single, consistent source. Pattern recognition techniques have been demonstrated to be effective in distinguishing and classifying human settlements. However, these methods are not ideal as they perform poorly when presented with imagery of the same area acquired at different dates. The poor generalization ability is mainly caused by large off-nadir viewing angles which produce image pairs with different viewing- and illumination-geometries. Classification performance is also decreased by differences in shadow length and orientation. The objective of this research is to improve the generalisation ability of the automated classification of human settlements using only remote sensing data over urban areas. The multiresolution local binary patterns (LBPs) algorithm, extended with an orthogonal variance measure for measuring local contrast features (i.e., the extended LBP) has been shown to excel at texture classification tasks. To minimize the viewing- and illumination-geometry effects and improve settlement classification, the extended LBP was applied to high spatial resolution panchromatic aerial images. The addition of a contrast component to the LBP features does not directly affect the desired invariance to shadow orientation and length, but it is expected that the richer features will nevertheless improve settlement classification accuracy. The extended LBP method was evaluated using a support vector machine (SVM) classifier for cross-date (training and test images of the same area acquired at different dates) and samedate analysis. For comparable results, LBPs without contrast features were also evaluated. The results showed the extended LBP to have a strong spatial and temporal generalisation ability for classifying settlements of aerial images, when compared to its counterpart. From this research, we can conclude that the extended LBP’s additional contrast features can improve overall settlement type classification accuracy and generalisation ability.