The effect of wavelet-based dimension reduction on neural network classification and subpixel targeting algorithms

The effect of using Adaptive Wavelets is investigated for dimension reduction and noise filtering of hyperspectral imagery that is to be subsequently exploited for classification or subpixel analysis. The method is investigated as a possible alternative to the Minimum Noise Fraction (MNF) transform as a preprocessing tool. Unlike the MNF method, the wavelet-transformed method does not require an estimate of the noise covariance matrix that can often be difficult to obtain for complex scenes (such as urban scenes). Another desirable characteristic of the proposed wavelet transformed data is that, unlike Principal Component Analysis (PCA) transformed data, it maintains the same spectral shapes as the original data (the spectra are simply smoothed). In the experiment, an adaptive wavelet image cube is generated using four orthogonal conditions and three vanishing moment conditions. The classification performance of a Derivative Distance Squared (DDS) classifier and a Multilayer Feedforward Network (MLFN) neural network classifier applied to the wavelet cubes is then observed. The performance of the Constrained Energy Minimization (CEM) matched-filter algorithm applied to this data us also observed. HYDICE 210-band imagery containing a moderate amount of noise is used for the analysis so that the noise-filtering properties of the transform can be emphasized. Trials are conducted on a challenging scene with significant locally varying statistics that contains a diverse range of terrain features. The proposed wavelet approach can be automated to require no input from the user.