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.
