Dim target detection in IR image sequences based-on fractal and rough set theory

This paper addresses the problem of detecting small, moving, low amplitude in image sequences that also contain moving nuisance objects and background noise. Rough sets (RS) theory is applied in similarity relation instead of equivalence relation to solve clustering issue. We propose fractal-based texture analysis to describe texture coarseness and locally adaptive threshold technique to seek latent object point. Finally, according to temporal and spatial correlations between different frames, the singular points can be filtered. We demonstrate the effectiveness of the technique by applying it to real infrared image sequences containing targets of opportunity and evolving cloud clutter. The experimental results show that the algorithm can effectively increase detection probability and has robustness.