Infrared small target detection in image sequences based on temporal low-rank and sparse decomposition

Yan Nie; Wei Li; Mingjing Zhao; Qiong Ran; Pengge Ma

doi:10.1117/12.2589426

27 January 2021 Infrared small target detection in image sequences based on temporal low-rank and sparse decomposition

Yan Nie, Wei Li, Mingjing Zhao, Qiong Ran, Pengge Ma

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117200A (2021) https://doi.org/10.1117/12.2589426
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

In infrared small target detection tasks, targets usually occupy very few pixels and present as local bright spots, lacking prior knowledge such as shape and speed. In response to the above problems, a temporal low-rank and sparse decomposition and spatio-temporal continuity detection algorithm, names as TLRSD-STC, is proposed to detect small targets and eliminate false alarm targets. The proposed algorithm firstly expands the sequence images in time domain. The preliminary separation of small targets and background is achieved through low-rank and sparse decomposition, and target prediction maps can be obtained. Subsequently, targets and noise are further separated by an improved pipeline filter to obtain the final detection image. The proposed algorithm is validated on three sequence images containing complex scenes. Experimental results demonstrate that the algorithm has a higher detection rate and lower false alarm rate than other algorithms in complex scenes.

Citation Download Citation

Yan Nie, Wei Li, Mingjing Zhao, Qiong Ran, and Pengge Ma "Infrared small target detection in image sequences based on temporal low-rank and sparse decomposition", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117200A (27 January 2021); https://doi.org/10.1117/12.2589426

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