Surveillance images downlinked from unmanned air vehicles (UAVs) may have corrupted pixels due to channel interferences from the adversary’s jammer. Moreover, the images may be deliberately downsampled in order to conserve the scarce bandwidth in UAVs. As a result, the automatic target recognition (ATR) performance may degrade significantly because of poor image quality due to corrupted and missing pixels. In this paper, we present some preliminary results of a novel approach to automatic target recognition based on corrupted images. First, we present a new matrix completion algorithm to reconstruct missing pixels in electro-optical (EO) images. Second, we extensively evaluated our algorithm using many EO images with different missing rates. It was observed that recovering performance in terms of peak signal-to-noise ratio (PSNR) is very good. Third, we compared with a state-of-the-art algorithm and found that our performance is superior. Finally, experiments using an ATR algorithm showed that the target detection performance (precision and recall) has been improved after applying our algorithm, as compared to those results generated by using interpolated images.
Jin Zhou, Bulent Ayhan, Chiman Kwan, and Trac Tran, "ATR performance improvement using images with corrupted or missing pixels," Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490E (Presented at SPIE Defense + Security: April 18, 2018; Published: 30 April 2018); https://doi.org/10.1117/12.2303659.
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