Robust Principal Component Analysis-Based Infrared Small Target Detection
Abstract
A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.
Cite
Text
Chen et al. "Robust Principal Component Analysis-Based Infrared Small Target Detection." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019925Markdown
[Chen et al. "Robust Principal Component Analysis-Based Infrared Small Target Detection." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/chen2019aaai-robust/) doi:10.1609/AAAI.V33I01.33019925BibTeX
@inproceedings{chen2019aaai-robust,
title = {{Robust Principal Component Analysis-Based Infrared Small Target Detection}},
author = {Chen, Qiwei and Wu, Cheng and Wang, Yiming},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {9925-9926},
doi = {10.1609/AAAI.V33I01.33019925},
url = {https://mlanthology.org/aaai/2019/chen2019aaai-robust/}
}