Paper Info
Title
Infrared Dim and Small Target Detection from Complex Scenes via Multi-Frame Spatial-Temporal Patch-Tensor Model
Abstract
Infrared imaging plays an important role in space-based early warning and anti-missile guidance due to its particular imaging mechanism. However, the signal-to-noise ratio of the infrared image is usually low and the target is moving, which makes most of the existing methods perform inferiorly, especially in very complex scenes. To solve these difficulties, this paper proposes a novel multi-frame spatial-temporal patch-tensor (MFSTPT) model for infrared dim and small target detection from complex scenes. First, the method of simultaneous sampling in spatial and temporal domains is adopted to make full use of the information between multi-frame images, establishing an image-patch tensor model that makes the complex background more in line with the low-rank assumption. Secondly, we propose utilizing the Laplace method to approximate the rank of the tensor, which is more accurate. Third, to suppress strong interference and sparse noise, a prior weighted saliency map is established through a weighted local structure tensor, and different weights are assigned to the target and background. Using an alternating direction method of multipliers (ADMM) to solve the model, we can accurately separate the background and target components and acquire the detection results. Through qualitative and quantitative analysis, experimental results of multiple real sequences verify the rationality and effectiveness of the proposed algorithm.
Year
DOI
Venue
2022
10.3390/rs14092234
REMOTE SENSING
Keywords
DocType
Volume
infrared image sequences, dim and small target detection, complex background
Journal
14
Issue
ISSN
Citations 
9
2072-4292
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yuxin Hu116.77
Yapeng Ma200.34
Zongxu Pan3748.13
Yuhan Liu400.34