Real-time expert system and neural network for the classification of remotely sensed dataThe paper examines software techniques for classifying remotely sensed data such that the number of computational steps and the amount of resources are bounded. The combination of both neural network and expert system methodology for classifying these data based on land use/land cover categories is examined. The method involves pipelining images through a neural net for initial classification and then through the expert system which resolves the ambiguous classifications. As with any pipeline, every component must have approximately equivalent run-times or otherwise a bottleneck will occur. If real-time is a requirement, each of the components must execute within a bounded number of steps. Attention is focused on the real-time system technique, which is argued to prevent a bottleneck for this data classification application.
Document ID
19920056770
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Short, Nicholas, Jr. (NASA Goddard Space Flight Center Greenbelt, MD, United States)