Spectroscopy and Spectral Analysis, Volume. 42, Issue 2, 609(2022)
A Combination of Multiple Deep Learning Methods Applied to Small-Sample Space Objects Classification
Fig. 1. Schematic diagrams of optical telescope (a) and terminal box (b)
Fig. 2. Hyperspectral images of a space object at different wavelengths
Fig. 3. The brightness of space object varying with wavelength
Fig. 4. The basic architecture diagram of DBSCAN
Fig. 5. The basic flow chart of generative adversarial network
Fig. 5. The original and generated spectra of a space object
Fig. 6. The basic flow chart of one dimensional CNN
Fig. 7. The basic flow chart of experimental algorithm
Fig. 8. Comparisons of average operation time and accuracy among various methods
Fig. 9. Accuracies of four combination methods
Fig. 9. Average operation time of four combination methods
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Shi-yu DENG, Cheng-zhi LIU, Yong TAN, De-long LIU, Nan ZHANG, Zhe KANG, Zhen-wei LI, Cun-bo FAN, Chun-xu JIANG, Zhong LÜ. A Combination of Multiple Deep Learning Methods Applied to Small-Sample Space Objects Classification[J]. Spectroscopy and Spectral Analysis, 2022, 42(2): 609
Category: Research Articles
Received: Jan. 8, 2021
Accepted: Feb. 7, 2021
Published Online: Apr. 2, 2022
The Author Email: DENG Shi-yu (dengsy@cho.ac.cn)