Small Target Detection in a Single Infrared Image Based on RPCA

doi:10.3969/j.issn.1000-1093.2016.09.028

Abstract

Abstract: The robust infrared small target detection is one of the key techniques of infrared search and track systems. An improved algorithm is presented for small target detection in single-frame infrared image. The infrared image model is generalized to a new infrared patch-image model, and based on the new model, the small target detection is formulated as an optimal robust principal component analysis (RPCA) problem of separating low-rank and sparse matrices. Considering the presence of noises and clutter in infrared image, an alternating direction algorithm is used for solving the RPCA problem to obtain sparse target image, and a simple image segmentation method is used to segment the target image for target detection. The small infrared targets in different scenes, such as sky, sea-sky, cloud and sea surface, are detected and simulated. Experimental results demonstrate the effectiveness of the proposed method.

Key words: ordnance science and technology, infrared image, small target detection, patch image model, low rank matrix recovery, robust principal component analysis

CLC Number:

O439

WANG Zhong-mei, YANG Xiao-mei , GU Xing-fa. Small Target Detection in a Single Infrared Image Based on RPCA[J]. Acta Armamentarii, 2016, 37(9): 1753-1760.

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