A two-layer detection model for infrared slow low-altitude targets

This paper proposes a novel detection approach for dim targets with low signal-to-noise ratios in an image sequence. Initially, the superposition analysis is introduced to reveal the relationship between target energy and noise energy in the overlapped images, which is vital for the effectiveness of singular value decomposition, and also the relationship of signal-to-noise ratios to angles between singular value vectors is analyzed, which illustrates the essence of angle-based detection methods. Second, analyzing the feasibility of locating targets using singular vectors, thus the first few singular vectors and threshold technology are combined to reconstruct the targets in each overlapped image, and then the positions of the suspected targets are connected to form tracks, which is validated in terms of Hough transform. Extensive experiments show that the proposed method not only works more stably under different signal-to-noise ratios, but also has better detection performance compared with the conventional baseline methods.