Robust infrared small target detection using local steering kernel reconstruction. (May 2018)

• Record Type: Journal Article
• Authors: Li, Yansheng; Zhang, Yongjun
• Is Part Of: Pattern recognition. Volume 77(2018:May)
• Page Start: 113
• Page End: 125
• Publication Date: 2018-05
• Subjects: Infrared small target detection -- Local steering kernel (LSK) -- Closed-form feature reconstruction; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.12.012
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper advocates the local steering kernel to encode the image patch. A novel local adaptive contrast measure using local steering kernel is proposed. This paper presents a patch-based infrared small target detection approach. The proposed infrared small target detection method achieves promising results. Abstract: Because infrared small target detection plays a crucial role in infrared monitoring and early warning systems, it has been the subject of considerable research. Although many infrared small target detection approaches have been proposed, how to robustly detect small targets in poor quality infrared images remains a challenge. Since existing feature descriptors are often sensitive to the quality of infrared images, this paper advocates the use of a local steering kernel (LSK) to encode the infrared image patch because the LSK method can provide robust estimation of local intrinsic structure, even for poor quality images. Furthermore, this paper proposes a novel local adaptive contrast measure based on LSK reconstruction (LACM-LSK) for infrared small target detection. To demonstrate the effectiveness of the proposed approach, a diverse test dataset, including six infrared image sequences with different backgrounds, was collected. Extensive experiments on the test dataset confirm that the proposed infrared small target detection approach can achieve better detection performance than state-of-the-art approaches.