Gradient field divergence-based small target detection in infrared images

Abstract

Infrared small target detection in complex cloud backgrounds has long been a research challenge. A novel robust target detection method based on the divergence of gradient field is proposed to enhance the target and suppress the complex background synchronously. The negative gradient field of the target intensity (NIG field) matches with characteristics of the positive source. The cloud cluster, on the other hand, lacks this feature. First, the NIG field is calculated based on the target’s property from the original image. The divergence values of NIG field are then calculated to produce a defined divergence map (D map), which highlights the target regions while suppressing the clutter regions. Meanwhile, a local vectors angle measure (LVAM) operator of the NIG field is designed to measure the angle distribution of 8-neighbour vectors and eliminate false target areas. Then, the defined local angle map (LA map) is obtained by measuring the local angle value of 8-neighbour vectors for each patch of NIG field. In addition, the divergence-local angle map (D-LA map) is obtained as the Hadamard product of the D map and LA map. Finally, we can easily obtain the target via a constant false alarm ratio based on the D-LA map. The performance evaluation results of real image sequences show that the proposed method is satisfactory for clutter suppression and target detection. Moreover, the results from comparative experiments show that the proposed method outperforms conventional methods in terms of detection accuracy and false alarm rate.