Pattern Classification from Raster Data Using Vector Lenses, Neural Networks and Expert Systems
Satellite data is often used for a variety of applications in assessing the condition of the state of the earth’s surface or the immediate atmosphere. This problem is usually attacked by processing the raw data (infrared, photographs, radar data, etc.) into a pixel stream and then composing a picture, which is used by experts to form the appropriate decisions. Such applications in oceanography include the assessment of ocean and ice states, in geography in terrestrial resource evaluation, in meteorology in cloud classification, etc.
The assessment of such pictures usually requires an expert whose decisions are based on experience, which is difficult to quantify into algorithmic form or even to reduce to a set of rules. Replicating this experience demands a system which can be taught the non quantifiable aspects of the classification procedure.
This paper deals with the problem of automatically classifying large pictures by utilizing several applications of artificial intelligence. The logical architecture of the classification scheme consists of a system of linear neurons acting as vector lenses, which provide input to a neural network. This combination acts both to reduce the dimensionality of the pixel stream representing the picture and to learn the various classifications. The expert system is used to provide expert opinion in case of difficult or inconclusive decisions and to adjust the parameters of the lens system to account for pattern inhomogeneity.
An analysis of the computational complexity of neural computing  shows that large pixel streams demand very large data storage and performance in achieving classical back-propagation training. This large computational complexity renders impossible the direct utilization of neural networks. The immediate need is for a reduction of the dimensionality of the pixel space.
The representation of a picture by a non orthogonal set of vectors  establishes the theoretical background for the lens concept. Many types of lenses are possible depending on the application. For classification, a particularly simple choice is the set of prototypical patterns representative of each class to be identified.
Result are presented for a simulation of a set of cloud patterns. The robustness and generalization of the system is explored. These results indicate that a robust system can be generated that can consistently perform classifications.
A design methodology is presented which acts as a guide line for adapting this approach to other similar problems.
KeywordsManifold Radar Remote Sensing Acoustics Archie
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