Statistical accuracy of an extraction algorithm for linear image objects

Abstract

Informal unpaved roads in developing countries arise naturally through human movement and informal housing setups. These roads are not authorised nor maintained by council, nor recorded in official databases or online maps. Mapping such roads from satellite images is a common problem, as information on these roads is critical for sustainable city growth. Information on their location and extent may be gleaned from spatial big data, however, no automatic or semi-automatic approach is freely available. This research develops a novel algorithm for extracting informal roads from multispectral satellite images, using physical road characteristics. These include near-infrared reflectance, addressed via the NDVI index, shape, addressed via measures of compactness and elongation, and grey-value intensity. The crux of the algorithm is the Discrete Pulse Transform, implemented via the Roadmaker's Pavage. The algorithm provides a classification of road objects, along with an associated uncertainty measure for each road object. Accuracy is assessed using per-pixel assessment metrics and metrics based on road characteristics, including completeness, correctness, and Pratt's Figure of Merit, which is applied to road extraction accuracy for the first time. The algorithm is applied to areas in Gauteng and North West Provinces, South Africa. Sources of uncertainty and error are discussed, such as indefinite boundaries, surface type heterogeneity, trees and shadows.