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.
Nowadays, it is a reality to launch, operate, and utilize small satellites at an affordable cost. However, bandwidth constraint is still an important challenge. For instance, multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, which is addressed mainly by source and channel coding aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis technique on the satellite to decide which information is effectively useful for specific target applications, before coding and transmitting. The challenge would be detecting clouds and vessels having the measurements of red-band, green-band, blue-band, and near infrared band, aiming at sufficient probability of detection, avoiding false alarms. Furthermore, the embedded platform constraints must be satisfied. Experiments for typical scenarios of summer and winter days in Stockholm, Sweden, are conducted using data from the Mimir's Well, the Saab AI-based data fusion system. Results show that non-relevant content can be identified and discarded, pointing out that for the cloudy scenarios evaluated, up to 73.1% percent of image content can be suppressed without compromising the useful information into the image. For the water regions in the scenarios containing vessels, results indicate that a stringent amount of data can be discarded (up to 98.5%) when transmitting only the regions of interest (ROI).
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