A Robust Multi-Classifier Decision Fusion Framework for Hyperspectral, Multi-Temporal Classification

Multi-source data fusion in the context of automatic target recognition (ATR) involves the fusion of multiple, independent observations of a phenomenon. If the collection of sources is diverse, the resulting classification system is expected to perform better than one based on any one source. In recent work, the authors have demonstrated the use of such decision fusion strategies in alleviating the over-dimensionality and small-sample-size problems associated with hyperspectral data. Multi-temporal hyperspectral recognition and classification tasks are even more prone to over-dimensionality of features and small training sample size problems. In this work, the authors will extend their previously proposed framework to multi-temporal, hyperspectral target recognition / classification problems. The performance of the proposed system will be compared against that of conventional hyperspectral feature extraction techniques. The efficacy of the proposed system is quantified by overall recognition accuracies.