Infrared Small Target Detection Using Double-Weighted Multi-Granularity
  Patch Tensor Model With Tensor-Train Decomposition
Guiyu Zhang Qunbo Lv Zui Tao Baoyu Zhu Zheng Tan Yuan Ma
Infrared small target detection plays an important role in the remote sensing
fields. Therefore, many detection algorithms have been proposed, in which the
infrared patch-tensor (IPT) model has become a mainstream tool due to its
excellent performance. However, most IPT-based methods face great challenges,
such as inaccurate measure of the tensor low-rankness and poor robustness to
complex scenes, which will leadto poor detection performance. In order to solve
these problems, this paper proposes a novel double-weighted multi-granularity
infrared patch tensor (DWMGIPT) model. First, to capture different granularity
information of tensor from multiple modes, a multi-granularity infrared patch
tensor (MGIPT) model is constructed by collecting nonoverlapping patches and
tensor augmentation based on the tensor train (TT) decomposition. Second, to
explore the latent structure of tensor more efficiently, we utilize the
auto-weighted mechanism to balance the importance of information at different
granularity. Then, the steering kernel (SK) is employed to extract local
structure prior, which suppresses background interference such as strong edges
and noise. Finally, an efficient optimization algorithm based on the
alternating direction method of multipliers (ADMM) is presented to solve the
model. Extensive experiments in various challenging scenes show that the
proposed algorithm is robust to noise and different scenes. Compared with the
other eight state-of-the-art methods, different evaluation metrics demonstrate
that our method achieves better detection performance in various complex
scenes.