High performance computing algorithms for land cover dynamics using remote sensing data
Global and regional land cover studies need to apply complex models on selected subsets of large volumes of multi-sensor and multi-temporal data sets that have been derived from raw instrument measurements using widely accepted pre-processing algorithms. The computational and storage requirements of most of these studies far exceed what is possible on a single workstation environment. We have been pursuing a new approach that couples scalable and open distributed heterogeneous hardware with the development of high performance software for processing, indexing and organizing remotely sensed data. Hierarchical data management tools are used to ingest raw data, create metadata and organize the archived data so as to automatically achieve computational load balancing among the available nodes and minimize input/output overheads. We illustrate our approach with four specific examples. The first is the development of the first fast operational scheme for the atmospheric correction of Landsat Thematic Mapper scenes, while the second example focuses on image segmentation using a novel hierarchical connected components algorithm. Retrieval of the global Bidirectional Reflectance Distribution Function in the red and near-infrared wavelengths using four years (1983 to 1986) of Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land data is the focus of our third example. The fourth example is the development of a hierarchical data organization scheme that allows on-demand processing and retrieval of regional and global AVHRR data sets. Our results show that substantial reductions in computational times can be achieved by the high performance computing technology.
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