High Performance Computing Algorithms for Land mCover Dynamics Using Remote Sensing Data

Abstract

Global and regional land cover studies require the ability to apply complex models on selected subsets of large amounts 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 such 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 I/O 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 TM scenes, while the second example focuses on image segmentation using a novel hierarchical connected components algorithm. Retrieval of global BRDF (Bidirectional Reflectance Distribution Function) in the red and near infrared wavelengths using four years (1983 to 1986) of Pathfinder AVHRR Land (PAL) data set 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 improvements in computational times can be achieved by using the high performance computing technology. (Also cross-referenced as UMIACS-TR-98-18)