@inproceedings{7e5d4748e1924cc7885070b2fad9bdae,
title = "A Novel Approach to Lighten the Onboard Hyperspectral Anomaly Detector",
abstract = "Hyperspectral image (HSI) anomaly targets detection is always applied for timeliness and onboard mission. For high detection accuracy, deep learning based HSI anomaly detectors (ADs) are widely employed in recent researches. However, their huge network scale for high-level representation ability leads to great computation burden for the onboard computation system. To decrease the computation complexity of the detector, a lightweight network is expected for the HSI AD. In this paper, by creating a multiobjective optimization with nondominated sorting genetic algorithm II (NSGA-II), an automatic evolution based deep learning network HSI AD (Auto-EDL-AD) is proposed to explore a lightweight network. The experimental results on an HSI dataset show that the proposed Auto-EDL-AD can generate an optimal network for the HSI anomaly detection which reaches up{\^A} to 170\% speedup without any detection accuracy loss.",
keywords = "Deep learning, Hyperspectral image, Multiobjective optimization, Real-time processing",
author = "Ning Ma and Yu Peng and Shaojun Wang and Jingyi Dong",
note = "Publisher Copyright: {\textcopyright} 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 10th EAI International Conference on Wireless and Satellite Systems, WiSATS 2019 ; Conference date: 12-01-2019 Through 13-01-2019",
year = "2019",
doi = "10.1007/978-3-030-19156-6\_41",
language = "英语",
isbn = "9783030191559",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "432--445",
editor = "Min Jia and Qing Guo and Weixiao Meng",
booktitle = "Wireless and Satellite Systems - 10th EAI International Conference, WiSATS 2019, Proceedings",
address = "德国",
}