Principal component analysis of multispectral images using neural network

The conventional approach of PCA applied to multispectral images involves the computation of the spectral image covariance matrix and application of diagonalization procedures for extracting the eigenvalues and corresponding eigenvectors. When the number of spectral images grows significantly, the matrix computation and manipulation become practically inefficient and inaccurate due to round-off errors. These deficiencies make the conventional scheme inefficient for this application. We propose a neural network model that performs the PCA directly from the original spectral images without any additional non-neuronal computations or preliminary matrix estimation. The design of the network topology and input/output representation as well as the design of learning algorithms are carefully established. The convergence of the model is studied. Its application has been realized on real multispectral images. The obtained results show that the model performs well.