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Proceedings Paper

Vector Quantization Training By A Self-Organizing Neural Network
Author(s): Thomas W Ryan; Charles A Cotter
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Paper Abstract

This paper presents work in progress on the application of a self-organizing neural network to vector quantization (VQ) training. A modified version of Kohonen's self-organizing feature map algorithm was applied to simulated data and to digitized Synthetic Aperture Radar image data. Preliminary results indicate that the network-based algorithm is potentially more robust than the traditional LBG training algorithm, especially when confronted with multimodal input data distributions.

Paper Details

Date Published: 12 October 1988
PDF: 10 pages
Proc. SPIE 0924, Recent Advances in Sensors, Radiometry, and Data Processing for Remote Sensing, (12 October 1988); doi: 10.1117/12.945700
Show Author Affiliations
Thomas W Ryan, Science Applications International Corporation (United States)
Charles A Cotter, Science Applications International Corporation (United States)


Published in SPIE Proceedings Vol. 0924:
Recent Advances in Sensors, Radiometry, and Data Processing for Remote Sensing
Philip N. Slater, Editor(s)

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