Chen Zhang, Xiuzai Zhang, Changjun Yang. Remote Sensing Image Cloud and Cloud Shadow Detection Method Based on RDA-Net Model[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2028005
- Laser & Optoelectronics Progress
- Vol. 58, Issue 20, 2028005 (2021)
Fig. 1. Structure of ResBlock and ResUnit. (a) ResBlock; (b) ResUnit
Fig. 2. Structure of PAM
Fig. 3. Structure of CAM
Fig. 4. Structure of R-ASPP
Fig. 5. Strecture of RDA-Net
Fig. 6. Structure of RU-Net
Fig. 7. Experimental dataset. (a) Remote sensing image block; (b) label
Fig. 8. Enhanced effects of experimental data. (a) Original image; (b) vertical rotation; (c) horizontal rotation; (d) horizontal and vertical rotation; (e) transformation of brightness; (f) injection of noise; (g) transformation of saturation; (h) transformation of color
Fig. 9. Experimental flow of cloud and cloud shadow detection
Fig. 10. Relationship between overall accuracy and number of iterations
Fig. 11. Comparison of detection results of Gaofen-1 WFV remote sensing image cloud under different methods. (a) Original images; (b) FCN-8s method; (c) K-means method; (d) SegNet method; (e) DeepLab method; (f) RU-Net method; (g) RDA-Net method; (h) cloud tags
Fig. 12. Visual comparison of cloud shadow detection results of Gaofen-1 WFV remote sensing image under two methods. (a) Original image; (b) RU-Net method; (c) RDA-Net method; (d) cloud and cloud shadow labels
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Table 1. Quantitative comparison results of cloud detection by different methods
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Table 2. Quantitative comparison of cloud shadow detection by different methods
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