TY - GEN
T1 - Airport aircraft target detection based on space spectrum feature fusion
AU - Zhang, Ning
AU - Xie, Shaobiao
AU - Luo, Huanlin
AU - Zhu, Xinzhong
AU - Qi, Naiming
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Due to the complexity of airport background, the traditional method of aircraft target detection usually brings a lot of false alarms or missed detection. In this paper, the full convolution network is used to extract spatial features, which are combined with spectral features, and the active learning method is used to select the hyperspectral image target detection algorithm of training samples. By combining the spectral characteristics of pixels and the spatial correlation between adjacent pixels, the comprehensive features which can reflect the spatial spectral joint characteristics of pixels are extracted, and the expression ability of pixel features is improved. The experimental results on multiple data sets show that the proposed method is suitable for the detection of small and weak targets with certain structural information, and has a good effect on the detection of aircraft targets in airport background.
AB - Due to the complexity of airport background, the traditional method of aircraft target detection usually brings a lot of false alarms or missed detection. In this paper, the full convolution network is used to extract spatial features, which are combined with spectral features, and the active learning method is used to select the hyperspectral image target detection algorithm of training samples. By combining the spectral characteristics of pixels and the spatial correlation between adjacent pixels, the comprehensive features which can reflect the spatial spectral joint characteristics of pixels are extracted, and the expression ability of pixel features is improved. The experimental results on multiple data sets show that the proposed method is suitable for the detection of small and weak targets with certain structural information, and has a good effect on the detection of aircraft targets in airport background.
KW - Active learning
KW - Feature fusion
KW - Fully conventional networks
KW - Hyperspectral detection
UR - https://www.scopus.com/pages/publications/85087524941
U2 - 10.1109/ICCCS49078.2020.9118479
DO - 10.1109/ICCCS49078.2020.9118479
M3 - 会议稿件
AN - SCOPUS:85087524941
T3 - 2020 5th International Conference on Computer and Communication Systems, ICCCS 2020
SP - 967
EP - 971
BT - 2020 5th International Conference on Computer and Communication Systems, ICCCS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Computer and Communication Systems, ICCCS 2020
Y2 - 15 May 2020 through 18 May 2020
ER -