Mapping of settlements in high-resolution satellite imagery using high performance computing

Classifying urban land cover from high-resolution satellite imagery is challenging, and those challenges are compounded when the imagery databases are very large. Accurate land cover data is a crucial component of the population distribution modeling efforts of the Oak Ridge National Laboratory's (ORNL) LandScan Program. Currently, LandScan Program imagery analysts manually interpret high-resolution (1-5 m) imagery to augment existing satellite-derived medium (30 m) and coarse (1 km) resolution land cover datasets. For LandScan, the high-resolution image archives that require interpretation are on the order of terabytes. The goal of this research is to automate human settlement mapping by utilizing ORNL's high performance computing capabilities. Our algorithm employs gray-level and local edge-pattern co-occurrence matrices to generate texture and edge patterns. Areas of urban land cover correlate with statistical features derived from these texture and edge patterns. We have parallelized our algorithms for implementation on a 64-node system using a single instruction multiple data programming model (SIMD) with Message Passing Interface (MPI) as the communication mode. Our parallel-configured classifier performs 30-40 times faster than stand-alone alternatives. We have tested our system on IKONOS imagery. The early results are promising, pointing towards future large-scale classification of human settlements at high-resolution.