多层感知机结合辐射传输模型的复杂陆地表面云检测

作者：Deng Meng-Jiao; Xu Xin^*; Ma Ying-Ying; Gong Wei; Jin Shi-Kuan; Hu Rui-Min

来源：Acta Electronica Sinica, 2022, 50(4): 932-942.

DOI：10.12263/DZXB.20210636

• Cloud detection
• MERSI II
• MODIS
• Multilayer perceptron
• Radiative transfer simulation

Cloud detection is a key step in the preprocessing of satellite remote sensing data. This paper proposes a cloud detection method by combining a multilayer perceptron with a radiative transfer model. The method is to identify cloud from moderate resolution satellite image using visible and near-infrared band reflectance information. In this method, firstly, the santa barbara DISORT atmospheric radiative transfer model(SBDART) is used to simulate and obtain datasets of reflectance values for a variety of complex terrestrial surfaces, which provides training samples for the multilayer perceptron. Secondly, the trained network model is used to distinguish cloud pixels from total pixels of the advanced medium Resolution Spectral Imager(MERSI II) image in the FengYun3D satellite MERSI II image, and then verified using vertical feature mask(VFM) product of the cloud-aerosol LIDAR infrared pathfinder satellite observations satellite(CALIPSO) and compared horizontally with the cloud mask product(MYD35) of the moderate resolution imaging spectroradiometer(MODIS). The results show that the accuracy of cloud detection for the multilayer perceptron is 76.25%, and especially this method works best in summer and low latitudes, achieves an accuracy of 91.74% for surface identification near the equator. In this paper, the method is more effective in detecting clouds under complex surface type conditions such as urban, farmland and bare soil, with accuracies of 83.37%, 84.52% and 73.11% respectively, which are higher than the 83.25%, 83.31% and 72.66% of the MYD35 product respectively. To further validate the effectiveness of the multilayer perceptron combined with the radiative transfer model, the training samples obtained from the radiative transfer model simulations are used in the k-nearest neighbors, Naive Bayesian, and Random Forest algorithms, respectively, and compared with the multilayer perceptron algorithm in this paper. The results show that the combination of the multilayer perceptron and the radiative transfer model has a higher accuracy. ? 2022, Chinese Institute of Electronics. All right reserved.

• 单位
  测绘遥感信息工程国家重点实验室; 武汉科技大学