A modular clutter rejection technique for FLIR imagery using region-based principal component analysis

In this paper, a modular clutter rejection technique using region-based principal component analysis (PCA) is proposed. Our modular clutter rejection system uses dynamic ROI (region of interest) extraction to overcome the problem of poorly centered targets. In dynamic ROI extraction, a representative ROI is moved in several directions with respect to the center of the potential target image to extract a number of ROIs. Each module in the proposed system applies region-based PCA to generate the feature vectors, which are subsequently used to decide about the identity of the potential target. We also present experimental results using real-life data evaluating and comparing the performance of the clutter rejection systems with static and dynamic ROI extraction.