Hyperspectral feature space partitioning via mutual information for data fusion

Multi-source data fusion is being actively explored in the remote sensing community for robust automatic target recognition (ATR) and other similar applications. Such an approach exploits multiple, independent observations of a phenomenon and performs a feature level or a decision level fusion for ATR, scene classification, land cover mapping, etc. In this paper, we present a method that utilizes such fusion techniques to exploit hyperspectral data, which otherwise typically suffers from the small sample size problem, (i.e., there are typically not as many ground truth pixels as the dimensionality of the data). In this work, we study the efficacy of using higher order statistical information (using average mutual information) for a bottom up band grouping in a multi- classifier setup. The band grouping procedure is employed to partition the hyperspectral space into approximately independent subspaces. A classifier is assigned to each subspace in the partition. Final classification decisions are made by fusing local decisions from each subspace. The goal of this paper is to (1) perform subspace identification using the proposed mutual information based metric, (2) explore the effect of the design parameters on the fusion performance and, (3) compare the performance of decision level fusion with feature level fusion over the partitioned subspace.