Cloud Detection of RGB Color Remote Sensing Images Based on Improved M-Net

Jingfeng Hu; Xiuzai Zhang; Changjun Yang

doi:10.3788/LOP56.162804

Laser & Optoelectronics Progress, Volume. 56, Issue 16, 162804(2019)

Cloud Detection of RGB Color Remote Sensing Images Based on Improved M-Net

Jingfeng Hu², Xiuzai Zhang^1,2, and Changjun Yang^3、*

¹ Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China

² School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China

³ National Satellite Meteorological Center, Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 100081, China

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Figures & Tables(11)

Fig. 1. Convolution unit and residual unit. (a) Forward propagation convolution unit; (b) residual unit

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Fig. 2. RM-Net network structure

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Fig. 3. DDCN network structure

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Fig. 4. Dataset enhancement. (a) Original image; (b) horizontal flip; (c) vertical flip; (d) horizontal and vertical flips; (e) saturation adjustment; (f) brightness adjustment; (g) color adjustment; (h) add noise

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Fig. 5. Overall accuracy curves

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Fig. 6. Visual comparison of Landsat8 image cloud detection results obtained by six methods

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Fig. 7. Visual comparison of GaoFen-1 WFV image cloud detection results obtained by six methods

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Fig. 8. Detection results of cloud and cloud shadow

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Table 1. Quantitative comparison of Landsat8 image cloud detection results obtained by six methods

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Table 1. Quantitative comparison of Landsat8 image cloud detection results obtained by six methods

Method	P_Precision	R_Recall	A_Accuracy	F_1score
k-means	0.8366	0.6585	0.8396	0.7369
CNN+SP	0.8605	0.9025	0.9064	0.8704
FCN2s	0.9293	0.8734	0.9243	0.9005
M-Net	0.9432	0.9091	0.9673	0.9258
DDCN	0.9322	0.9283	0.9728	0.9302
RM-Net	0.9334	0.9509	0.9816	0.9418

Table 2. Quantitative comparison of GaoFen-1 WFV image cloud detection results obtained by six methods

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Table 2. Quantitative comparison of GaoFen-1 WFV image cloud detection results obtained by six methods

Method	P_Precision	R_Recall	A_Accuracy	F_1score
k-means	0.7499	0.7154	0.8356	0.7322
CNN+SP	0. 8413	0.8635	0.8914	0.8523
FCN2s	0.9019	0.8935	0.9238	0.8976
M-Net	0.9307	0.9039	0.9590	0.9132
DDCN	0.9316	0.9273	0.9654	0.9294
RM-Net	0.9265	0.9353	0.9762	0.9309

Table 3. Quantitative comparison of detection results of cloud and cloud shadow
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Table 3. Quantitative comparison of detection results of cloud and cloud shadow
Method P'_Precision R'_Recall A'_Accuracy F'_1score
DDCN 0.7734 0.7116 0.9356 0.7412
RM-Net 0.8657 0.7942 0.9703 0.8284

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Jingfeng Hu, Xiuzai Zhang, Changjun Yang. Cloud Detection of RGB Color Remote Sensing Images Based on Improved M-Net[J]. Laser & Optoelectronics Progress, 2019, 56(16): 162804

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Paper Information

Category: Remote Sensing and Sensors

Received: Jan. 27, 2019

Accepted: Apr. 9, 2019

Published Online: Aug. 5, 2019

The Author Email: Changjun Yang (yangcj@cma.gov.cn)

DOI:10.3788/LOP56.162804

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Quantitative comparison of Landsat8 image cloud detection results obtained by six methods

Table 1. Quantitative comparison of Landsat8 image cloud detection results obtained by six methods

Table 2. Quantitative comparison of GaoFen-1 WFV image cloud detection results obtained by six methods

Table 2. Quantitative comparison of GaoFen-1 WFV image cloud detection results obtained by six methods

Table 3. Quantitative comparison of detection results of cloud and cloud shadow

Table 3. Quantitative comparison of detection results of cloud and cloud shadow