Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

• Published in: IEEE access Vol. 8; pp. 149963 - 149983
• Main Authors: Yang, Chunping, Kong, Xuan, Cao, Zhaoyang, Peng, Zhenming
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: ADMM; cirrus detection; Clouds; Clutter; False alarms; Image segmentation; Infrared analysis; Infrared cirrus (astronomy); Infrared imagery; Infrared imaging; Ket augments; Landsat satellites; Mathematical analysis; Matrix decomposition; Norms; Object detection; Optimization; Principal component analysis; Principal components analysis; Remote sensing; Satellite imagery; Satellites; Target detection; Target recognition; Tensile stress; Tensor multi-mode expansion sum nuclear norm; Tensors; wavelet transform; Wavelet transforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3015975

Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales. In this article, an infrared cirrus detection method based on the tensor robust principal component analysis model (TRPCA) is proposed. The method treats multiple bands of remote sensing data as tensors, but classical tensor nuclear norms cannot represent the tensor rank well; therefore, we use tensor multi-mode expansion sum nuclear norm (TMESNN) to approximate the tensor rank better. First, a set of Landsat-8 data is transformed into a tensor model, and a TRPCA model is constructed by TMESNN and the <inline-formula> <tex-math notation="LaTeX">L_{1} </tex-math></inline-formula> norm. Then, this model is solved by Ket augments and the alternating direction method of multipliers (ADMM). Finally, Mallat wavelet transform is used to supplement information and remove clutter, and the final detection result is obtained by adaptive threshold segmentation. Compared with other optimization-based methods, this method has better detection performance and accuracy.