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由Sun等人提出的统一样本云检测方法，基于AVIRIS高光谱样本库模拟出待检测传感器的云和晴空地表像元，将模拟得到的多光谱样本数据输入到BP神经网络中进行逐像元分类，生成云检测模型，实现Landsat 8 OLI等宽光谱传感器较高精度的云检测。该方法基于统一样本模拟出不同传感器的样本像元库，适用于多种传感器的云检测。由于Landsat 8 OLI波段较多，波谱范围覆盖宽，容易实现云的高精度识别。为了进一步提高其在光谱范围较窄的GF-6 WFV数据上的云检测应用精度，在模拟出的样本库中加入GF-6 WFV数据典型高亮地表像元。通过目视解译对云检测结果进行精度验证，结果表明，该算法利用可见光和近红外通道的遥感数据可以高精度的识别出植被、水体、建筑、裸地等地表类型上空的厚云、碎云和薄云。改进后的云检测算法，云像元平均正确率达到88.40%，在高亮地表上空云像元正确率达到87.40%，在不同地表类型上空的云像元平均正确率为92.60%。结果表明，加入高反射率地物的算法可以利用有限波段实现云和地表的高精度分离。
Abstract: The unified sample cloud detection method proposed by Sun et al., based on the AVIRIS hyperspectral sample database to simulates the cloud and clear sky surface pixels of the sensor to be detected, and inputs the simulated multi-spectral sample data into BP neural network for pixel-by-pixel classification to generate cloud detection models, this method can realize high-precision cloud detection of Landsat 8 OLI and other wide spectrum sensor. This method simulates the sample pixel libraries is suitable for cloud detection of various sensors. As Landsat 8 OLI sensor has more bands, the spectrum covers wide range, it is easy to realize high-precision cloud identification. In this paper, an improved cloud detection algorithm is proposed. Due to the narrow spectral range of GF-6 WFV data and the lack of cloud sensitive bands such as 1.38, 1.65 and thermal infrared bands, cloud identification accuracy in high reflectivity area is low. The cloud and clear sky image metadata simulated based on the unified sample pixel database have weak ability to identify clouds and bright surface, and it is unable to realize stable and high-precision cloud identification of this type of satellite. In order to further improve the application precision of cloud detection on GF-6 WFV data with narrow spectral range, GF-6 WFV data typical highlighted surface pixels were added into the simulated sample base to realize cloud detection of GF-6 WFV data with high precision. The main work contents of the improved unified sample cloud detection algorithm are as follows :(1) GF-6 WFV multi-spectral data simulation. This paper simulates the apparent reflectance of the corresponding band of GF-6 WFV data by the weighted synthesis of the band of AVIRIS hyperspectral data. (2) BP deep learning cloud detection model. In the simulated GF-6 WFV data sample base, typical highlighted surface samples such as bare land, buildings and snow in the GF-6 WFV data are added. Apparent reflectance of each band of the improved data pixel in the sample base is taken as input vector. The inductive ability of neural network is used to learn cloud detection rules, generate cloud detection model, and conduct cloud detection experiments. The accuracy of cloud detection results was verified by visual interpretation. The artificial labeled cloud area was taken as the reference truth value, and the cloud detection results of the algorithm were compared with the reference truth value on a per-pixel basis. By constructing error matrix, the precision of cloud inspection results of the proposed algorithm is calculated and verified. With the improved cloud detection algorithm, the average correct rate of cloud pixels reaches 88.40%, 87.40% for cloud pixels over the highlighted land surface, and 92.60% for cloud pixels over different land surface types. The results show that the algorithm can accurately identify the thick clouds, broken clouds and thin clouds over vegetation, water, buildings and bare land with high precision by using the remote sensing data of visible and near-infrared channels. The algorithm of adding high-reflectivity ground objects can use limited bands to achieve high-precision separation of clouds and ground.