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This paper presents a novel Sparsity-driven joint Image REgistration and Change Detection (SIRE-CD) technique for SAR imagery. The proposed algorithm simultaneously performs two main tasks: (i) locally register the test and reference images; and (ii) perform the change detection between the two. The key innovative concept here is the sparsity-driven transformation of the signatures from the reference image to match to those of the test image at the local image patch level. In other words, we are constructing a large dictionary from the reference data and use that to find the sparsest representation that best approximates the new incoming test data. The accuracy level of the approximation determines the detected changes between the reference and the test image. We demonstrate the performance of this technique using both simulated data and real SAR imagery from the Army Research Laboratory ultra-wideband (UWB) SAR forward-looking radar.
Date of Conference: 14-19 March 2010