Robust detection of infrared small and dim targets with highly heterogeneous backgrounds plays an indispensable role in infrared search and tracking (IRST) system, which is still a challenging problem. In this article, a novel method based on multisubspace learning and spatial-temporal tensor data structure is aimed to solve this problem. First, a tensor data structure is constructed to use inner correlation in spatial and temporal domain of the infrared image sequence. Second, in consideration of the complex and heterogeneous backgrounds in infrared images, the proposed method promotes the multisubspace property to tensor domain to separate the target and the background more accurately. Finally, an efficient and effective optimization algorithm based on alternating direction method of multipliers (ADMM) is designed to solve this problem. Experimental results on various and real scenes demonstrate the superiority of the proposed method compared to other five baseline methods.

Infrared small target detection is an important fundamental task in the infrared system. Therefore, many infrared small target detection methods have been proposed, in which the low-rank model has been used as a powerful tool. However, most low-rank-based methods assign the same weights for different singular values, which will lead to inaccurate background estimation. Considering that different singular values have different importance and should be treated discriminatively, in this article, we propose a nonconvex tensor low-rank approximation (NTLA) method for infrared small target detection. In our method, NTLA regularization adaptively assigns different weights to different singular values for accurate background estimation. Based on the proposed NTLA, we propose asymmetric spatial–temporal total variation (ASTTV) regularization to achieve more accurate background estimation in complex scenes. Compared with the traditional total variation approach, ASTTV exploits different smoothness intensities for spatial and temporal regularization. We design an efficient algorithm to find the optimal solution for our method. Compared with some state-of-the-art methods, the proposed method achieves an improvement in terms of various evaluation metrics. Extensive experimental results in various complex scenes demonstrate that our method has strong robustness and a low false-alarm rate.

The infrared small and dim targets are often buried in strong clutters and noise, which requires robust and efficient detection approaches to achieve search and track task. In this paper, a novel infrared small target detection approach based on tensor robust principal component analysis (RPCA) is proposed by fully utilizing both spatial and temporal information. Traditional total variation (TV) regularization based method only considers the spatial information of single frame, and its efficiency cannot meet the real-time processing requirement. We incorporate an anisotropic spatial-temporal TV regularization to utilize both single-frame and inter-frame information. Firstly, the input image sequence is transformed to an infrared patch tensor (IPT) model. Then, the spatial-temporal TV regularization and weighted IPT model (STTV-WNIPT) is proposed to separate the target and background. For the low-rank background component, we adopt the weighted tensor nuclear norm and spatial-temporal TV regularization to describe the smoothness. For sparse target component and noise component, we use the l1 norm and Frobenius norm terms to characterize, respectively. Finally, the proposed model can be solved efficiently by the Alternating Direction Method of Multipliers (ADMM). Extensive experiments demonstrate that the proposed model outperforms the other competitive methods, and it is much more efficient than traditional TV based method.

The robustness of infrared small-faint target detection methods to noisy situations has been a challenging and meaningful research spot. The targets are usually spatially small due to the far observation distance. Considering the underlying assumption of noise distribution in the existing methods is impractical; a state-of-the-art method has been developed to dig out valuable information in the temporal domain and separate small-faint targets from background noise. However, there are still two drawbacks: (1) The mixture of Gaussians (MoG) model assumes that noise of different frames satisfies independent and identical distribution (i.i.d.); (2) the assumption of Markov random field (MRF) would fail in more complex noise scenarios. In real scenarios, the noise is actually more complicated than the MoG model. To address this problem, a method using the non-i.i.d. mixture of Gaussians (NMoG) with modified flux density (MFD) is proposed in this paper. We firstly construct a novel data structure containing spatial and temporal information with an infrared image sequence. Then, we use an NMoG model to describe the noise, which can be separated with the background via the variational Bayes algorithm. Finally, we can select the component containing true targets through the obvious difference of target and noise in an MFD maple. Extensive experiments demonstrate that the proposed method performs better in complicated noisy scenarios than the competitive approaches.

Satellite videos play important roles in many applications in recent years due to the advantages of continuous providing high temporal resolution remote sensing images. Although many progresses have been achieved for moving object detection (MOD) in satellite videos, the low-rank characteristics of background and the intensity variations of moving objects across frames have not been fully exploited. In this paper, we propose an efficient method for MOD in satellite videos, which models the background with enhanced 3D total variation E-3DTV) regularization and the moving objects with Gaussian prior. Specifically, considering that the gradient maps on the spatial and temporal dimensions exhibit different physical meanings, we model the background with different Laplacian sparsity priors for the gradient maps along the spatial and temporal dimensions for 3DTV regularization. Different from current methods which model moving objects with sparsity characteristics in each frame alone, we utilize Gaussian prior to model intensity changing characteristics of moving objects across frames. After integrating background model and moving object model into low-rank sparse matrix factorization framework, the alternating direction method of multipliers (ADMM) is adopted to iteratively optimize the parameters of background and moving object models. We conduct experiments on VISO and SkySat datasets, and the results demonstrate that our method achieves superior MOD performance with high computational efficiency compared to state-of-the-art methods.