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
- Spectroscopy and Spectral Analysis
- Vol. 42, Issue 2, 609 (2022)
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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Table 1. Rough classification results based on DBSCAN
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Table 2. Rough classification results based on K-means
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Table 3. Classification accuracies (%) of six categories by using multiple methods combination
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