Potential application of remote sensing in monitoring informal settlements in South Africa where complimentary data does not exist

Remotely sensed images are used for many purposes in today's world. In this paper, we explore the potential application of high resolution satellite images in extracting features and classifying urban settlements. The test area is Soweto, an urban area in the Greater Johannesburg Metropolitan area, in Gauteng, South Africa. We propose a new settlement typology for efficient classification of formal and informal settlements via QuickBird satellite images. Following on, an automated classification procedure based on the local binary pattern texture features is introduced. Using a convenience sample of 25 images, we show the feasibility of the new typology by applying it to both a manual classification procedure and an automated one. The manual classification procedure was conducted by a group of five experts who interpreted the images and classified them according to formal and informal settlements. Analysis of the results revealed an overall mean classification accuracy of 99.2% with a standard deviation of 1.79%. The automated method involved extracting tiles at random positions within the 25-sample dataset. The features extracted from these tiles were classified using a support vector machine. Classification accuracy on new samples was 56.27%, but cross-validation on the training data reached classification accuracies of 98%.