INFRARED SMALL-TARGET DETECTION BASED ON THREE-ORDER TENSOR CREATION AND TUCKER DECOMPOSITION

Robust infrared small-target detection has always been a research hotspot in target search and tracking systems. However, the image itself has low signal-to-noise ratio (SNR), and the targets usually lack detailed/texture information. In addition, the background is complex and diverse. All the above factors make it easy for the targets to be submerged. In this paper, a novel method is proposed based on a three-order creation and the Tucker decomposition. First, the morphological profiles (i.e., area attribute and height attribute of max-tree) are applied to create a three-order tensor, which compensates for the lack of detailed information by supplementing spatial information in infrared images. Then, the Tucker decomposition is employed on the created tensor, in which most of the background can be estimated and eliminated from three dimensions. Finally, the target is detected on the remaining and the results of diverse morphological profiles are fused, which further enhances the target information. Experimental results demonstrate the effectiveness of the proposed method.