Distributed high-performance computation for remote sensing

Abstract

We describe distributed and parallel algorithms for processing remotely sensed data such as geostationary satellite imagery. We have built a distributed data repository based around the client-server computing model across wide-area ATM networks, with embedded parallel and high-performance processing modules. We focus on algorithms for classification, geo-rectification, correlation and histogram analysis of the data. We consider characteristics of image data collected from the Japanese GMS5 geostationary meteorological satellite, and some analysis techniques we have applied to it. As well as providing a browsing interface to our data collection, our system provides processing and analysis services on-demand. We are developing our system to carry out processing and data reduction services at-a-distance, enabling remote users with limited bandwidth, access to our system, to obtain useful derived data products at the resolution they require. Our target hardware consists of a heterogeneous collection of distributed workstations, multi-processor servers and massively parallel computers at locations through- out Australia. These platforms are connected by ATM-based LANs and also through ATM switches across long distanceWANssuchas T elstra'sExperimental Broad- band Network, connecting Adelaide, Melbourne and Canberra. Our particular in- terest in constructing remote data access and processing services is the potential to utilisesuch resources as are available to a given user, yielding the best performance compromise of data processing and data delivery. Tothisend, we are buildinga set of resource scheduling and management utilities that will integrate the processing modules we describe. We have considered a number of software frameworks for building our integrated system and are focusing on a distributed object model using WWW protocols and the Java language. Keywords: Remote-sensing, ATM, Classification, Client-Server, Satellite Imagery. © 1997 ACM.