Multiple Kernel Discriminant Analysis and Decision Fusion for Robust Sub-Pixel Hyperspectral Target Recognition

Hyperspectral image based automatic target recognition (ATR) systems often project the high dimensional hyperspectral reflectance signatures onto a lower dimensional subspace using techniques such as principal components analysis (PCA), Fisher´s linear discriminant analysis (LDA) and stepwise LDA. In a general classification framework, these projections are sub-optimal, and in the absence of sufficient training data, are likely to be ill conditioned. In recent work, the authors proposed a divide and conquer approach that partitions the hyperspectral space into contiguous subspaces followed by multi-classifiers and decision fusion. Although this technique alleviated the small sample size problem and provided a good recognition performance in light and moderate pixel mixing, the performance significantly decreased under severe mixing conditions, as does with conventional ATR techniques. In this work, the authors propose a kernel discriminant analysis based projection in each subspace of the partition, followed by the multi-classifier, decision fusion framework to ensure robust recognition even in severe pixel mixing conditions. The performance of the proposed system (as measured by overall target recognition accuracies) is greatly superior to conventional dimensionality reduction techniques, as well as the more recently proposed divide-and-conquer technique.