Adaptive filtering approaches for multispectral image classification based on eigen-feature

In the study, we proposed two adaptive classifiers based on image eigen-features for multispectral image classification. An adaptive signal subspace projection (ASSP) approach is first proposed 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 artificial neural networks (ANN) 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 BPMLP classifiers have lower detection error and fast convergence rate than conventional Wiener filter and per-pixel ANN methods.