Infrared Small Target Detection Using Nonoverlapping Patch Spatial–Temporal Tensor Factorization With Capped Nuclear Norm Regularization

Abstract

Infrared target detection technology is indispensable in precision guidance and infrared warning systems. It faces grant challenges under complex scenes, such as poor robustness against a variety of scenes, inaccurate separation of the target from similar goals, etc. To solve these problems, we present a novel small target detection method based on spatial–temporal information of infrared imageries. A nonoverlapping patch spatial–temporal tensor (NPSTT) model is established by sliding a window to obtain nonoverlapping patches in adjacent images. Moreover, the tensor capped nuclear norm (TCNN) is introduced, which approximates the tensor rank more accurately for local infrared images. TCNN regularization and NPSTT (TCNN-NPSTT) are adopted to detect the potential targets. Essentially, the procedure of extracting targets from the background is converted to the low rank and sparse tensor factorization. Besides, an efficient optimization scheme utilizing the alternating direction multiplier method (ADMM) is introduced to solve the proposed model. Experiments in various scenes show that NPSTT can obtain better performance against complex backgrounds than state-of-the-art baselines, using the evaluating indicators include the receiver operating characteristic curve (ROC), signal-to-clutter ratio gain (SCRG), and background suppression factor (BSF).