Built-up index and fractal dimension clustering of multispectral satellite urban images

The urbanization variability of several European cities is quantified in terms of fractal dimension (Formula presented.) and built-up density (Formula presented.) by using the WorldView (WV-2 European Cities and WV-ESA archive) multispectral satellite imagery collections at different spatial resolutions. The robustness and accuracy of the proposed approach are further assessed by analyzing Sentinel-2 L2A (S-2) data. The fractal dimension (Formula presented.) is estimated by implementing the Detrending Moving Average Algorithm (DMA). The built-up density (Formula presented.) is estimated by using the Masked Built-up Extraction Index (MBEI). The analysis yields values of (Formula presented.) and (Formula presented.) in the ranges (Formula presented.) and (Formula presented.), respectively, for dispersed and highly urbanized areas. The minimum volume ellipsoids (MVE) criterion is adopted to cluster the values of fractal dimension (Formula presented.) and built-up index (Formula presented.) and classify different urbanization areas.