Adaptive Filtering Approaches to Multispectral Image Classification

In the study, two adaptive classifiers based on image eigen-features are proposed for multispectral image classificationm. One is based on a liner filter with weights adaptively updated by the principal eigencompoments, and the other is an artificial neural network (ANN) with weights trained by the image eigen-features. We first propose an adaptive signal subspace projection (ASSP) approach to detect and extract target signatures in unknown background. The weights of ASSP are adjusted adaptively by using the eigen-features which are updated recursively by the adaptive eigen-decomposition algorithm. Then, we proposed an ANN classifier based on back propagation multilayer perception (BPMLP) with weights trained by the image eigen-features. Simulation results validate the image eigen-features can alleviate the noise effect in classification and the proposed ASSP and ANN classifiers have lower detection error and fast convergence rate than conventional Wiener filter and per-pixel ANN methods.