Detecting informal settlements from high resolution imagery using an object based image approach
The aim of this study was twofold: evaluate different approaches to deriving normalised digital surface models (nDSM) and develop a robust and transferable methodology for mapping informal dwellings. In the first component, three approaches to extract nDSMs were investigated: (i) light detection and ranging (LiDAR) data, (ii) high resolution aerial photographs in a process of image matching, and (iii) a series of aerial images captured using a hand held camera using structure from motion (SfM) techniques. SfM is a relatively new technique that has not been widely used for nDSM extraction. This study represented a first attempt at evaluating the three approaches, particularly for mapping informal dwellings. The accuracy of the respective nDSMs was evaluated using vertical profiles, area based, as well as positional based accuracy assessment metrics. This provided a clear indication of the robustness of each of the models. Results showed that an nDSM can be successfully extracted in an informal settlement for informal dwelling mapping. Overall LiDAR achieved the highest accuracy in all three accuracy assessments, showing its ability to handle the undefined and complex morphology of informal settlements. To further test the robustness of the nDSMs, each model was applied to an independent test site with varying dwelling density and achieved improved accuracies. In the second component, the utility of high resolution WorldView 2 imagery and object based image analysis (OBIA) techniques to develop a robust and transferable methodology for mapping individual informal dwellings in the City of Cape Town was tested. A systematic approach was used to objectively identify segmentation and classification parameters. The supervised segmentation parameter tuner (SPT) tool was used to derive optimal segmentation parameters, and was evaluated using an area based accuracy assessment which resulted in high compactness 86% and correctness >88% To reduce data dimensionality and optimize the classification process, the RF algorithm reduced the original WV 2 feature set (n=40) and aerial imagery (n=60) feature sets by 23% and 53% whereas the CART algorithm reduced the same feature set by 95% and 91% respectively. For classification, a supervised approach was adopted using the random forest (RF) algorithm, and a rule based classification using a rule set in eCognition software. Although different feature subsets were selected by the RF and CART algorithm for the WV 2 and aerial imagery, similar classification accuracies were achieved in all the test sites.