Infrared Dim and Small Target Detection Based on Strengthened Robust Local Contrast Measure

被引：16

作者：

Li, Zehao ^{[1
]}

Liao, Shouyi ^{[1
]}

Zhao, Tong ^{[1
]}

机构：

[1] Rocket Force Univ Engn, Dept Automat, Xian 710025, Peoples R China

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2022年 / 19卷

关键词：

Sparse matrices; Object detection; Clutter; Weight measurement; Convex functions; Temperature; Image edge detection; Fuzzy matrix; infrared dim and small target detection; local contrast measurement; low-rank and sparse representation;

D O I：

10.1109/LGRS.2021.3136887

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

Infrared small target detection is a popular issue in the field of computer vision. Traditional methods have been researched a lot and can basically deal with target detection in usual scenarios. However, small and dim targets do not have obvious characteristics and are easily interfered by clutter in a complex background. This letter studied common target detection methods such as low-rank and sparse representation as well as local contrast measure (LCM), a new method called strengthened robust local contrast measure (SRLCM) algorithm is proposed here. By solving non-convex problem more rigorously and analyzing small target features carefully, SRLCM can achieve a better detection and segmentation performance in an irregular background.

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