SVD-based scattered small targets detection

Due to lack of sufficient shape and strength information about the targets present in the entire image obtained in a wide range of scenarios over a long distance, it is very hard to detect these targets using the existing detection methods. In order to solve this problem, this paper proposes a scattered small target detection algorithm based on singular value decomposition. First, the detectability of a single target is analyzed from the perspective of singular values; second, the detectability of scattered targets is analyzed in terms of singular values and elementary transformations, based on the composability of dispersed targets, the dispersed targets can be combined into a large target using elementary transformations, on this basis, the conclusion is given that the image containing dispersed targets and the image containing a large target have the same detectability; then perform singular value decomposition on the image that may contain dispersed targets and the standard residual image which does not contain targets, and obtain their respective singular value vectors, use these singular value vectors to calculate the cosine angle which was used to determine whether the image contains targets or not, if it is confirmed, the targets are roughly located based on the singular vectors. Finally, the effectiveness of the algorithm is verified using Monte Carlo simulation.