The expectation of this research is to greatly broaden the use of remotely sensed imagery by providing a novitiate user, access to embedded information and knowledge without embarking upon a full-scale research project to complete the content extraction, storage and retrieval process. The intent of our approach is to develop an intelligent system that can adapt to changes or new information and learn from these changes. This will drastically alter the approach researchers take in using any digital imagery by opening the scientific discovery process, particularly to disciplines that have not traditionally used imagery due to the complexity of the image processing techniques. We hope to accomplish this by the judicious use of declarative and procedural knowledge, engineering, and automatic feature or image object labeling using recent classification techniques on BEOWULF parallel computing architectures.
Campbell, William J. and Chettri, Samir
"Knowledge Base Data Mining and Machine Learning in a Parallel Computing Environment,"
Online Journal of Space Communication: Vol. 2
, Article 12.
Available at: https://ohioopen.library.ohio.edu/spacejournal/vol2/iss3/12
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