8 October 2015 Detection of dual-band infrared small target based on joint dynamic sparse representation
Author Affiliations +
Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96751C (2015) https://doi.org/10.1117/12.2199368
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China
Infrared small target detection is a crucial and yet still is a difficult issue in aeronautic and astronautic applications. Sparse representation is an important mathematic tool and has been used extensively in image processing in recent years. Joint sparse representation is applied in dual-band infrared dim target detection in this paper. Firstly, according to the characters of dim targets in dual-band infrared images, 2-dimension Gaussian intensity model was used to construct target dictionary, then the dictionary was classified into different sub-classes according to different positions of Gaussian function’s center point in image block; The fact that dual-band small targets detection can use the same dictionary and the sparsity doesn’t lie in atom-level but in sub-class level was utilized, hence the detection of targets in dual-band infrared images was converted to be a joint dynamic sparse representation problem. And the dynamic active sets were used to describe the sparse constraint of coefficients. Two modified sparsity concentration index (SCI) criteria was proposed to evaluate whether targets exist in the images. In experiments, it shows that the proposed algorithm can achieve better detecting performance and dual-band detection is much more robust to noise compared with single-band detection. Moreover, the proposed method can be expanded to multi-spectrum small target detection.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jinwei Zhou, Jinwei Zhou, Jicheng Li, Jicheng Li, Zhiguang Shi, Zhiguang Shi, Xiaowei Lu, Xiaowei Lu, Dongwei Ren, Dongwei Ren, } "Detection of dual-band infrared small target based on joint dynamic sparse representation", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96751C (8 October 2015); doi: 10.1117/12.2199368; https://doi.org/10.1117/12.2199368

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