SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas

Very-High-Resolution (VHR) Remote Sensing (RS) data are crucial for deriving essential geospatial information on cities, e.g. for urban planning, population estimation and socioeconomic assessments with particular merit in sub-Saharan Africa (SSA) due to the scarcity or absence of reference data. One of the cornerstones of information that can be produced from RS is classified Land Use and Land Cover (LULC) maps. For VHR imagery, Object Based Image Analysis (OBIA) is the most efficient methodology to produce such outputs. A crucial intermediate step in OBIA is the selection of a suitable segmentation scale. However, for large, heterogeneous areas (e.g. at city level), little effort has been made to optimize OBIA algorithms. Supervised methods to optimize segmentation parameters are subjective and time consuming while Unsupervised Segmentation Parameter Optimization (USPO) techniques, assume spatial stationarity for the whole image. This is problematic for geographically large heterogenous areas and does not capture intra-urban variations due to building size, materials and fractions of LULC intrinsically varying in space. In this study, we employ a novel framework named Spatially Partitioned Unsupervised Segmentation Parameter Optimization (SPUSPO) that optimizes segmentation parameters locally for two SSA cities, Dakar and Ouagadougou. The framework employs the open access GRASS GIS software that is suitable for large scale computing. Our results suggest that SPUSPO is an efficient way to optimize segmentation parameters for large and heterogeneous urban areas.