@inproceedings{65acaccff08b40ab9feed744f5fb9429,
title = "A dim small target detection method based on spatial-frequency domain features space",
abstract = "The target detection, especially extracting low SNR potential targets and stars from the star images, plays as a key technology in the space debris surveillance. Due to the complexity of the imaging environment, the detection of dim small targets in star images faces many difficulties, including low SNR and rare unstable features. This paper proposes a dim small target detection method based on the high dimensional spatial-frequency domain features extracted by filter bank, and training the support vector machine (SVM) classifier. The experimental results demonstrate that the proposed method exceeds the state-of-the-art on the ability to detect low SNR targets.",
keywords = "Dim and small target detection, Filter bank, Support vector machine (SVM)",
author = "Jinqiu Sun and Danna Xue and Haisen Li and Yu Zhu and Yanning Zhang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 9th International Conference on Image and Graphics, ICIG 2017 ; Conference date: 13-09-2017 Through 15-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71589-6_16",
language = "英语",
isbn = "9783319715889",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "174--183",
editor = "Xiangwei Kong and Yao Zhao and David Taubman",
booktitle = "Image and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers",
}