Small Infrared Target Detection Based on Low-Rank and Sparse Matrix Decomposition

Abstract:

Sparse and low-rank matrix decomposition (SLMD) tries to decompose a matrix into a low-rank matrix and a sparse matrix, it has recently attached much research interest and has good applications in many fields. An infrared image with small target usually has slowly transitional background, it can be seen as the sum of low-rank background component and sparse target component. So by SLMD, the sparse target component can be separated from the infrared image and then be used for small infrared target detection (SITD). The augmented Lagrange method, which is currently the most efficient algorithm used for solving SLMD, was applied in this paper for SITD, some parameters were analyzed and adjusted for SITD. Experimental results show our algorithm is fast and reliable.

• Periodical: Applied Mechanics and Materials (Volumes 239-240)
• Pages: 214-218
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.239-240.214
• Online since: December 2012
• Authors: Cheng Yong Zheng, Hong Li
• Keywords: Augmented Lagrange Method, Infrared Target Detection, Matrix Decomposition, Robust PCA, Sparse

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