@inproceedings{7d5c1a8084194bef9c993a69f6ce120e,
title = "StarNet: Convolutional Neural Network for Dim Small Target Extraction in Star Image",
abstract = "The number of space debris increases greatly in the last decades due to the intense outer space exploration, making a deteriorating earth orbit. The detecting, dodging and removing of space debris become a remarkable international issue. Among them, the detection of extremely dim target is still an open question. In this paper, we propose a novel dim target extraction method in single-frame star image based on convolutional neural network. The network is designed to extract the features of different spatial scales, the feature maps are up-sampled by deconvolution, and the multi-layer feature maps are fused to achieve the pixel-level classification. Experiments show that the method proposed outperforms the state-of-the-art especially on the dim target detection.",
keywords = "Convolutional neural network, Dim small target, Low SNR, Semantic segmentation, Star image",
author = "Danna Xue and Yushu Zheng and Jinqiu Sun and Yu Zhu and Yaoqi Hu and Yanning Zhang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th IEEE International Conference on Multimedia Big Data, BigMM 2018 ; Conference date: 13-09-2018 Through 16-09-2018",
year = "2018",
month = oct,
day = "18",
doi = "10.1109/BigMM.2018.8499101",
language = "英语",
series = "2018 IEEE 4th International Conference on Multimedia Big Data, BigMM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE 4th International Conference on Multimedia Big Data, BigMM 2018",
}