Gradient Field Divergence-based Small Target Detection in Infrared Images

Abstract Infrared small target detection in complex cloud backgrounds has always been a challenging research topic. To synchronously enhance the target and suppress the complex background, a novel robust target detection method based on the divergence of the gradient field is proposed. The negative gradient field of the target intensity (NIG field) matches with characteristics of the positive source. However, the cloud clutter lacks this characteristic. Firstly, based on the property of the target, the NIG field is calculated from the original image. Thereafter, the divergence values of NIG field are calculated to obtain a defined divergence map (D map), which highlights the target regions and suppresses 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 the NIG field. In addition, the divergence-local angle map (D-LA map) is obtained as the Hadamard product of D map and LA map. Finally, we can obtain the target conveniently via constant false alarm ratio (CFAR) based on the D-LA map. The performance evaluation results of real image sequences show that the proposed method is satisfactory in terms of clutter suppression and target detection. Moreover, the results from comparative experiments show that the proposed method is superior to conventional methods in terms of detection accuracy, false alarm rate, and running time.