Vector Quantization Training By A Self-Organizing Neural Network

Thomas W Ryan; Charles A Cotter

doi:10.1117/12.945700

12 October 1988 Vector Quantization Training By A Self-Organizing Neural Network

Thomas W Ryan, Charles A Cotter

Proceedings Volume 0924, Recent Advances in Sensors, Radiometry, and Data Processing for Remote Sensing; (1988) https://doi.org/10.1117/12.945700
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

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.

Citation Download Citation

Thomas W Ryan and Charles A Cotter "Vector Quantization Training By A Self-Organizing Neural Network", Proc. SPIE 0924, Recent Advances in Sensors, Radiometry, and Data Processing for Remote Sensing, (12 October 1988); https://doi.org/10.1117/12.945700

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