Decision Fusion With Confidence-Based Weight Assignment for Hyperspectral Target Recognition

Conventional hyperspectral image-based automatic target recognition (ATR) systems project high-dimensional reflectance signatures onto a lower dimensional subspace using techniques such as principal components analysis (PCA), Fisher's linear discriminant analysis (LDA), and stepwise LDA. Typically, these feature space projections are suboptimal. In a typical hyperspectral ATR setup, the number of training signatures (ground truth) is often less than the dimensionality of the signatures. Standard dimensionality reduction tools such as LDA and PCA cannot be applied in such situations. In this paper, we present a divide-and-conquer approach that addresses this problem for robust ATR. We partition the hyperspectral space into contiguous subspaces based on the optimization of a performance metric. We then make local classification decisions in every subspace using a multiclassifier system and employ a decision fusion system for making the final decision on the class label. In this work, we propose a metric that incorporates higher order statistical information for accurate partitioning of the hyperspectral space. We also propose an adaptive weight assignment method in the decision fusion process based on the strengths (as measured by the training accuracies) of individual classifiers that made the local decisions. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target recognition accuracies. The proposed system was found to significantly outperform conventional approaches. For example, under moderate pixel mixing, the proposed approach resulted in classification accuracies around 90%, where traditional feature fusion resulted in accuracies around 65%.