Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

Landan Zhang,Zhenming Peng

doi:10.3390/rs11040382

Abstract

Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

Highlights

Infrared small target detection is of great importance in many military applications, such as early-warning systems, missile-tracking systems, and precision guided weapons
To avoid the problem of equal treatment on singular values and reduce some biases, we develop a nonconvex infrared small target detection model based on partial sum of tensor nuclear norm (PSTNN), which can approximate the tensor rank better, and convert the detection task into a problem of solving the tensor robust principle component analysis model
Via minimizing the partial sum of singular values, NIPPS achieves a better performance than infrared patch-image model (IPI)

Summary

Introduction

Infrared small target detection is of great importance in many military applications, such as early-warning systems, missile-tracking systems, and precision guided weapons. Infrared small target detection is still full of challenges, which is mainly related to the following. Infrared small target detection methods can be divided into two categories: sequential-based and single-frame-based methods. Traditional sequential-based methods including pipeline filtering [1], 3D matched filtering [2], and multistage hypothesis testing [3] are applicable when the background is static and homogeneous, utilizing both spatial and temporal information to capture the target trajectory. In real applications, the movement between the target and imaging sensor is fast, coupled with various complex backgrounds, the performance of sequential-based methods degrades rapidly. Those methods are unable to meet the real-time requirements due to the usage of multiple frames. NRAM reaches the desired results except for the last sequence, from which the target is almost disappeared

Results

Discussion

Conclusion