Satellite images allow characterizing and monitoring urban slums. Yet the urban landscape as a complex geographic system is composed of hierarchical patterns and discrete objects in a spatial and temporal continuum with different scales and anisotropy which can only be estimated from image snapshots. Understanding the spatial heterogeneity of slums in terms of scale and anisotropy from discrete image pixels is nontrivial and has not been explicitly addressed by image-based studies detecting slums, where scale and direction in characterizing slum features are commonly done by trial and error. This study addresses this gap by analyzing the impact of scales and anisotropy detected in the scale space and frequency domain for the calculation of texture indices that ultimately govern the detection of slums. Employing case studies of three cities with a large portion of slum population and for which we have very high resolution satellite imagery, we identify the characteristic scales of slum and formal built-up areas. Results show that the characteristic scales correspond with the optimal grain size to obtain image texture features for detecting slums, while the directional spectral energy at the pixel level identifies characteristic directions. Thus texture indices calculated at the characteristic scale and along the characteristic directions of slum patterns improve the efficiency in feature extraction and classification of slums, where optimizing the scale has a higher impact on the detection of slums than choosing the optimal directions. This study provides a framework for scientifically selecting optimal scales and directions for slum mapping studies. The framework is recommended to be tested for more general applications in land surface characterization and classification especially by using high order texture indices.
|Number of pages||13|
|Journal||Computers, environment and urban systems|
|Publication status||Published - 1 Jan 2019|