Parallel image classification on the HIVE

Remotely sensed imagery represents a growing source of information to many practical applications. Technologies to rapidly process imagery data into useful information products has not kept pace with the rapidly growing volume and complexity of imagery data increasingly available from Government and commercial sources. Significant processing speed improvements have been achieved by implementation of classification methods on the highly-parallel integrated virtual environment (HIVE) - a Beowulf class system using parallel virtual machine software. This paper discusses our parallel processing architecture and how three different classification algorithms performed in this computing environment. Also discussed are conclusions and recommendations for future work to apply these techniques to more complex data and further improve the processing speeds.