Spectral identification by artificial neural network and genetic algorithm

Abstract

Abstract With the increasing utilization of multi-spectral imaging sensors, automatic identification of spectral signatures would be an invaluable facility. Conventional, linear approaches have been shown to be limited: although reasonably stable in the presence of small amounts of noise, their performance degrades severelyat high noise levelsor when several spectra are combined. Nonlinear approaches are therefore attractive. This paper describes a detailed investigation into the suitability of two nonlinear paradigms, namely artificial neural networks and genetic algorithms, for spectral identification. The ability of each method to distinguish between several different spectra is assessed, as is their stability to noise and their capacity to correctly identify combinations of spectra. These results are compared with a linear technique, cross-correlation. Both non-linear methods are shown to be very effective for spectral identification, the results being far superior to those of cross-correlation. However, it will be demonstrated that the genetic algorithm approach requires a greater understanding of the physical processes involved. A practical scheme, utilizing both neural network and genetic algorithm approaches, is suggested.