Object detection by radial basis neural network filtering of spectral data

Tom G. Thomas Jr.; M. Serkan Ozkan; Ye Tung; Mohammad Alam

doi:10.1117/12.677931

24 August 2006 Object detection by radial basis neural network filtering of spectral data

Tom G. Thomas Jr., M. Serkan Ozkan, Ye Tung, Mohammad Alam

Proceedings Volume 6312, Applications of Digital Image Processing XXIX; 631204 (2006) https://doi.org/10.1117/12.677931
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

An object recognition technique has been developed that allows the rapid screening of multispectral images for objects with known spectral signatures. The technique is based on the configuration of a radial basis neural network (RBN) that is specific for a particular object spectral signature or series of object spectral signatures. The method has been used to identify features in CASI-2 and HYDICE images with results comparable to conventional spatial object recognition techniques with a significant reduction in processing time. Radial basis neural networks have several advantages over the more common backpropagation neural networks, including better selectivity and faster training, resulting in a significant reduction in overall image processing time and greater accuracy.

Citation Download Citation

Tom G. Thomas Jr., M. Serkan Ozkan, Ye Tung, and Mohammad Alam "Object detection by radial basis neural network filtering of spectral data", Proc. SPIE 6312, Applications of Digital Image Processing XXIX, 631204 (24 August 2006); https://doi.org/10.1117/12.677931

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