Unsupervised mapping of a hybrid urban area in South Africa

Abstract

Hybrid urban areas are dominated by important spectral mixtures from formal and informal housing units which make them difficult to map even for the most robust classifier. Proposals to introduce other descriptive features, such as size, shape, texture, and context into the classification process, come with another drawback which is how to ensure the selected feature thresholds are optimal. Image segmentation which is the backbone of object-based analysis depends on a range of parameters including scale parameter, shape, smoothness, colour, and compactness weighting factors. Current techniques to select optimal segmentation scales only give the remote sensing analyst control over one parameter out of five (20%). This study proposes a classification strategy that gives the analyst control of 60% of the parameters to ensure an acceptable segmentation outcome. The study also proposes a feature selection approach that eliminates feature overlaps within the feature space which may not be observable within the original data. An automatic optimal parameter selection function is also proposed in this study. Tested on a SPOT5 resolution merge image, the approach overpowered the accuracy metrics of (Kemper et al.in Int Arch Photogramm Remote Sens Spat Inform Sci 40(7): 1389, 2015) with overall, sensitivity, specificity, precision, true skill statistic accuracies of respectively 0.97, 0.96, 1, 0.942, 0.95 against 0.97, 0.804, 0.98, 0.477, and 0.781. Similar trends are observed with the smallest average error of omission for built-up and non-built structures at 0.042 and 0 against to 0.196 and 0.164. The errors of commission for built-up and non-built-up structures were 0.060 and 0.008 respectively compared to 0.523 and 0.585.