A comparative study of the use of local directional pattern for texture-based informal settlement classification

In developing and emerging countries progression of informal settlements has been a fast growing phenomenon since the mid-1990s. Half of the world's population is housed in urban settlements. For instance, the growth of informal settlements in South Africa has amplified after the end of apartheid. In order to transform informal settlements to improve the living conditions in these areas, a lot of spatial information is required. There are many traditional methods used to collect these data, such as statistical analysis and fieldwork; but these methods are limited to capture urban processes, particularly informal settlements are very dynamic in nature with respect to time and space. Remote sensing has been proven to provide more efficient techniques to study and monitor spatial patterns of settlements structures with high spatial resolution. Recently, a new feature method, local directional pattern (LDP), based on kirsch masks, has been proposed and widely used in biometrics feature extraction. In this study, we investigate the use of LDP for the classification of informal settlements. Performance of LDP in characterizing informal settlements is then evaluated and compared to the popular gray level co-occurrence matrix (GLCM) using four classifiers (Naive-Bayes, Multilayer perceptron, Support Vector Machines, k-nearest Neighbor). The experimental results show that LDP outperforms GLCM in classifying informal settlements.