Classification of hyperspectral images using wavelet transforms and neural networks

The Airborne Visible InfraRed Imaging Spectrometer (AVIRIS), presently being flown by the Jet Propulsion Laboratory, acquires images of the earth in the visible and reflected infrared. The wavelengths of the measured radiation range from about 400 nm to 2400 nm and are divided into 224 contiguous channels having a nominal spectral bandwidth of 10 nm. This means a high resolution radiance spectrum is acquired for each 20 m X 20 m ground cell in the AVIRIS scene. Geologic mapping from such data is possible by classifying each pixel based on the distinctive spectral signatures recorded in the channels. Artificial neural networks (ANN) have used these spectra successfully to classify an AVIRIS subscene of the Lunar Craters Volcanic Field (LCVF) in Nye County, Nevada. The size and number of spectra in an AVIRIS scene makes classifying these images a computationally intensive task. By classifying the data in a compressed format, savings in computer time may be realized. The wavelet bases have the desirable property of rendering signals similar to the AVIRIS spectra sparse in the wavelet domain. In this investigation, the discrete wavelet transform was applied to the spectra. This produced a set of wavelet coefficients for the spectra that could be made sparse with seemingly little loss of accuracy. Small subsets of the wavelet coefficients were used to classify the LCVF scene by ANN. The degree to which information was lost in the wavelet transform and the elimination of wavelet coefficients from the classification was assessed by making comparisons between the different ANN classifications. The ANN was chosen over more conventional classifiers because of its proven sensitivity in distinguishing subtle but geologically relevant features in these spectra.