Extraction and classification of moving targets in multi-sensory MAMI-1 data collection

In this work we consider the problem of extraction and classification of moving targets in wide area imagery. We use the Air Force Research Laboratory's (AFRL) airborne multi-sensor dataset, MAMI-1, for testing, wherein moving targets mostly consist of people and vehicles. The movers are extracted using a novel sparse and low-rank matrix decomposition technique. We further compare the classification performance based on SIFT, Dense SIFT, and a superpixel based feature extraction. The results show the superpixel approach as the most advantageous.