This article presents an elementary change detection algorithm designed using a synchronous model of computation (MoC) aiming at efficient implementations on parallel architectures. The change detection method is based on a 2D-first-order autoregressive ([2D-AR(1)]) recursion that predicts one-lag changes over bitemporal signals, followed by a high-parallelized spatial filtering for neighborhood training, and an estimated quantile function to detect anomalies. The proposed method uses a model-based on the functional language paradigm and a well-defined MoC, potentially enabling energy and runtime optimizations with deterministic data parallelism over multicore, GPU, or FPGA architectures. Experimental results over the bitemporal CARABAS-II SAR UWB dataset are evaluated using the synchronous MoC implementation, achieving gains in detection and hardware performance compared to a closed-form and well-known complexity model over the generalized likelihood ratio test (GLRT). In addition, since the one-lag AR(1) is a Markov process, its extension for a Markov chain in multitemporal (n-lags) analysis is applicable, potentially improving the detection performance still subject to high-parallelized structures.
SAR processing usually requires very accurate navigation data, i.e. to form a focused image. The track must be measured within fractions of the centre wavelength. For high frequencies (e.g. X-band) this condition is too strict. Even with a cutting-edge motion measurement system, autofocus is a necessity. For low frequencies (e.g. VHF-band) a differential GPS (DGPS) is often an adequate solution (alone). However, for this case, it is actually conceivable to rely on autofocus capability over the motion measurement system. This paper describes how to form a SAR image without support from navigation data. That is within the scope of factorized geometrical autofocus (FGA). The FGA algorithm is a base-2 fast factorized back-projection realization with six free geometry parameters (per sub-aperture pair). These are tuned step-by-step until a sharp image is obtained. This procedure can compensate for an erroneous geometry (from a focus perspective). The FGA algorithm has been applied successfully on an ultra-wideband (UWB) data set, acquired at VHF-band by the CARABAS 3 system. The track is measured accurately by means of a DGPS. We however adopt and modify a basic geometry model. A linear equidistant flight path at fixed altitude is assumed and adjusted at several resolution levels. With this approach, we emulate a stand-alone processing chain without support from navigation data. The resulting FGA image is compared to a reference image and verified to be focused. This indicates that it is feasible to form a VHF-band SAR image without a motion measurement system.
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