Sample quality is the key to automated cloud detection from regional remote sensing images, and scale is one of the major impediments to sample quality control. In this paper, we select the southwest mountainous area in China, which is fragmented, cloudy, and rainy, as the study area. We proposed a method for constructing a cloud detection dataset based on the idea of downscaling and the spectral characteristics of vegetation. Finally, we validated the dataset by the U-Net+ deep learning model. The experimental results show that the cloud detection accuracy reaches 95.11% when using the dataset constructed in this paper, which is approximately 40% higher than the cloud detection accuracy with large-scale samples. Additionally, it reduced the workload of masking a large number of samples for a specific region and realizing the possibility of efficient cloud detection in the region.
In the agricultural field, optical remote sensing technology plays an important role in crop monitoring or production estimation. However, the widespread distribution of clouds and rain limits the application of optical remote sensing. Synthetic aperture radar (SAR) has been widely used for studies of oceans, atmosphere, land, and space exploration, as well as by the military due to its all-weather nature, penetration to surface and cloud layers, and diversity of information carriers. However, it is difficult to classify ground objects with high accuracy based on SAR data. Considering the features of these two datasets, we proposed a framework to improve crop classifications in cloudy and rainy areas based on the optical-SAR response mechanism. Specifically, this method is designed to train a parametric analytic model in the area using both kinds of datasets and applied in the area with only SAR data to obtain the optical time-series features. Then crops from the second area were classified by the long-short-term memory network. As an example, the parametric analytic model in Lixian County was studied and was applied to Xifeng County to classify the crops with the OA of 61%, which had proved the robustness of the method.
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