Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

Infrared small target detection is challenging due to the various background and low signal-to-clutter ratios. Considering the information deficiency faced by single spatial or temporal information, we construct a low false alarm spatial and temporal filter for infrared small target detection. A multiscale patch-based contrast measure is first used to suppress background and remove cloud edges at a coarse level. Then, a temporal variance filter is used to remove small broken cloud regions and suppress noise at a fine level. By integrating these two methods, infrared small targets can be extracted accurately and robustly using an adaptive threshold segmentation. The experimental results indicate that our proposed method can robustly detect small infrared targets with a low false alarm rate.