A Neural Network Approach to Sensitivity Analysis of AVIRIS Spectral Bands

James N. Etheredge

The purpose of the project described in this paper was to perform sensitivity analysis on the 224 bands collected by the Advanced Very High Resolution Imaging Spectrometer (AVIRIS) sensor. The sensitivity analysis was conducted utilizing artificial neural network technology. A baseline was established by performing partial training of a neural network using the equivalent six non-thermal TM bands as input. The remaining AVIRIS data was divided into nine groups of contiguous bands. The first, last and middle bands of each group were added to the baseline inputs and used to partially train a separate neural network using parameters identical to the baseline network. While several of the groups demonstrated a small (or even negative) impact on pixel classification, the presence of other groups improved the performance of the neural network. The results obtained support the viability of the neural network approach in ascertaining the sensitivity of band groups within the AVIRIS data.

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