A high performance image database system for remotely sensed imagery

We present the design of and performance results for an image database system for remotely sensed imagery. The system stores and serves level 1B remotely sensed data, providing users with a flexible and efficient means for specifying and obtaining image-like products on either a global or a local scale. We have developed both parallel and sequential versions of the system; the parallel version uses the CHAOS++ library, developed at the University of Maryland as part of an NSF Grand Challenge project, to support parallel object oriented programming.