Paper Info
Title
Three-Order Tensor Creation and Tucker Decomposition for Infrared Small-Target Detection
Abstract
Existing infrared small-target detection methods tend to perform unsatisfactorily when encountering complex scenes, mainly due to the following: 1) the infrared image itself has a low signal-to-noise ratio (SNR) and insufficient detailed/texture knowledge; 2) spatial and structural information is not fully excavated. To avoid these difficulties, an effective method based on three-order tensor creation and Tucker decomposition (TCTD) is proposed, which detects targets with various brightness, spatial sizes, and intensities. In the proposed TCTD, multiple morphological profiles, i.e., diverse attributes and different shapes of trees, are designed to create three-order tensors, which can exploit more spatial and structural information to make up for lacking detailed/texture knowledge. Then, Tucker decomposition is employed, which is capable of estimating and eliminating the major principal components (i.e., most of the background) from three dimensions. Thus, targets can be preserved on the remaining minor principal components. Image contrast is further enhanced by fusing the detection maps of multiple morphological profiles and several groups with discontinuous pruning values. Extensive experiments validated on two synthetic data and six real data sets demonstrate the effectiveness and robustness of the proposed TCTD.
Year
DOI
Venue
2022
10.1109/TGRS.2021.3057696
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Tensors, Vegetation, Shape, Level set, Entropy, Sparse matrices, Signal to noise ratio, Infrared image, multiple morphological profiles, small target detection, three-order tensor creation, Tucker decomposition
Journal
60
Issue
ISSN
Citations 
99
0196-2892
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Mingjing Zhao111.36
Wei Li2108888.08
Lu Li300.34
Pengge Ma401.35
Zhaoquan Cai500.68
Ran Tao6899100.20