The extraction of objects from advanced geospatial intelligence (AGI) products based on synthetic aperture radar (SAR) imagery is complicated by a number of factors. For example, accurate detection of temporal changes represented in two-color multiview (2CMV) AGI products can be challenging because of speckle noise susceptibility and false positives that result from small orientation differences between objects imaged at different times. These cases of apparent motion can result in 2CMV detection, but they obviously differ greatly in terms of significance. In investigating the state-of-the-art in SAR image processing, we have found that differentiating between these two general cases is a problem that has not been well addressed. We propose a framework of methods to address these problems. For the detection of the temporal changes while reducing the number of false positives, we propose using adaptive object intensity and area thresholding in conjunction with relaxed brightness optical flow algorithms that track the motion of objects across time in small regions of interest. The proposed framework for distinguishing between actual motion and misregistration can lead to more accurate and meaningful change detection and improve object extraction from a SAR AGI product. Results demonstrate the ability of our techniques to reduce false positives up to 60%.
Berkay Kanberoglu and David Frakes, "Extraction of advanced geospatial intelligence (AGI) from commercial synthetic aperture radar imagery," Proc. SPIE 10201, Algorithms for Synthetic Aperture Radar Imagery XXIV, 1020106 (Presented at SPIE Defense + Security: April 13, 2017; Published: 28 April 2017); https://doi.org/10.1117/12.2262359.
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