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Improvement of unified sample cloud detection technology and its application in GF-6 WFV cloud detection

  • role： First author第一作者
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

  • Email：1035016908@qq.com
  • Introduction：隋淞蔓，研究方向为大气定量遥感。E-mail： 1035016908@qq.com
SUI Songman,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

  • Email：571176273@qq.com
  • Introduction：夹尚丰，研究方向为大气定量遥感。E-mail： 571176273@qq.com
JIA Shangfeng*,  
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

HU Xueqian

résumé

The unified sample cloud detection method proposed by Sun et al. based on the AVIRIS hyperspectral sample database simulates the cloud and clear sky surface pixels of the sensor to be detected. The method also 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 sensors. This method, which simulates the sample pixel libraries, is suitable for cloud detection of various sensors. Given that the Landsat 8 OLI sensor has more bands, the spectrum covers a wide range, easily facilitating high-precision cloud identification.In this paper, an improved cloud detection algorithm is proposed. Owing to the narrow spectral range of GF-6 WFV data and the lack of cloud-sensitive bands such as 1.38 μm, 1.65 μm, and thermal infrared bands, cloud identification accuracy in high reflectivity areas is low. The cloud and clear sky image metadata simulated based on the unified sample pixel database have a weak ability to identify clouds and bright surfaces, and realizing stable and high-precision cloud identification of this type of satellite is impossible. To further improve the application precision of cloud detection on GF-6 WFV data with a 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 content of the improved unified sample cloud detection algorithm is 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. The apparent reflectance of each band of the improved data pixel in the sample base is taken as the input vector. The inductive ability of the neural network is used to learn cloud detection rules, generate a 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 an 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 0.884 and 0.874 for cloud pixels over the highlighted land surface and 0.926 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.

mots-clés

cloud detection;GF-6 WFV data;Hyperspectral pixel library;Bright surface;Deep learning algorithm

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