Structure Tensor-Based Infrared Small Target Detection Method for a Double Linear Array Detector

Abstract

The paper focuses on the mathematical modeling of a new double linear array detector. The special feature of the detector is that image pairs can be generated at short intervals in one scan. After registration and removal of dynamic cloud edges in each image, the image differentiation-based change detection method in the temporal domain is proposed to combine with the structure tensor edge suppression method in the spatial domain. Finally, experiments are conducted, and our results are compared with theoretic analyses. It is found that a high signal-to-clutter ratio (SCR) of camera input is required to obtain an acceptable detection rate and false alarm rate in real scenes. Experimental results also show that the proposed cloud edge removal solution can be used to successfully detect targets with a very low false alarm rate and an acceptable detection rate.

Infrared Small Target Detection Based on Tensor Tree Decomposition and Self-Adaptive Local Prior

“…(1) background consistency-based methods; (2) human visual system (HVS)-based methods; (3) deep learning (DL)-based methods; and (4) low-rank and sparse decomposition (LRSD)-based methods.…”

“…Meanwhile, compared with visible light systems, it boasts a range of advantages, such as exceptional anti-interference features and the ability to operate all throughout the day [1]. Owing to the superior benefits of infrared imaging, infrared dim and small target detection plays a significant role in military and civil applications, such as aerospace technology [2], security surveillance [3], and forest fire prevention [4]. However, due to the lengthy detection distance, infrared targets usually occupy only a few pixels and lack shape information and textural features.…”