Cloud detection, as a crucial step in preprocessing optical satellite images, plays a significant role in the subsequent application analysis. The increasingly enriched optical satellite remote sensing images pose a challenge in achieving quick cloud detection of numerous multi-source satellite remote sensing images. Given that conventional cloud detection exhibits low accuracy and limited universality, this study proposed a multi-scale feature fusion neural network model, i.e., the multi-source remote sensing cloud detection network (MCDNet). The MCDNet comprises a U-shaped architecture and a lightweight backbone network, and its decoder integrates multi-scale feature fusion and a channel attention mechanism to enhance model performance. The MCDNet model was trained using tens of thousands of globally distributed multi-source satellite images, covering commonly used satellite data like Google and Landsat data and domestic satellite data like GF-1, GF-2, and GF-5 data. Several classic semantic segmentation models were used for comparison with the MCDNet model in the experiment. The experimental results indicate that the MCDNet model exhibited superior performance in cloud detection, achieving detection accuracy of over 90% for all types of satellite data. Additionally, the MCDNet model was tested on the Sentinel data that were not used in training, yielding satisfactory cloud detection effects. This demonstrates the MCDNet model’s robustness and potential for use as a general model for cloud detection of medium- to high-resolution satellite images.
Tab.1 Dataset for cloud detection from multi-source remote sensing images
Fig.2 Training curve of the MCDNet
模型
P
R
F1
OA
IoU
SegNet
0.89
0.83
0.86
0.93
0.75
PSPNet
0.87
0.86
0.86
0.93
0.76
HRNetV2
0.94
0.85
0.89
0.94
0.80
UNet
0.85
0.94
0.89
0.95
0.80
BiSeNet
0.84
0.96
0.89
0.95
0.81
DeeplabV3+
0.91
0.90
0.91
0.95
0.83
MFGNet
0.91
0.91
0.91
0.96
0.84
MCDNet
0.93
0.95
0.94
0.97
0.89
Tab.2 Evaluation for cloud detection on multi-source remote sensing images
模型
P
R
F1
OA
IoU
MCDNet-withoutAT
0.92
0.92
0.92
0.96
0.85
MCDNet-Xcep
0.91
0.96
0.93
0.97
0.87
MCDNet-withoutDC
0.92
0.95
0.93
0.97
0.88
MCDNet
0.93
0.95
0.94
0.97
0.89
Tab.3 Evaluation for ablation experiments of MCDNet
卫星
0%云覆盖
20%云覆盖
真彩色影像
云检测结果
真彩色影像
云检测结果
Landsat
GF-5
GF-2
GF-1
Google
卫星
45%云覆盖
80%云覆盖
真彩色影像
云检测结果
真彩色影像
云检测结果
Landsat
GF-5
GF-2
GF-1
Google
Tab.4 Multi-source remote sensing true color images and MCDNet cloud detection results under different cloud coverage conditions
Fig.3 Cloud detection results of MCDNet on Sentinel-2 images
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2023,
Vol. 35
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