This paper presents a deep learning based method for inversion of Aerosol Optical Depth (AOD) from Landsat 8 OLI multi-band satellite data. Traditional aerosol inversion algorithms include the dark target method, the deep blue algorithm and other algorithms. The dark target method is good for areas with low surface reflectivity such as dense vegetation and water bodies, while the deep blue algorithm is usually used for inversion in areas with high reflectivity. Therefore, this study employs Deep belief networks (DBN) to learn the potential relationship between Landsat 8 OLI multi-band data and AOD. In this paper, Landsat 8 OLI remote sensing images with atmospheric AOD observations over the past 6 years (2013-2018) were collected as the training dataset. A deep confidence network structure is next designed for learning the mapping relationships from Landsat 8 OLI multi-band data to AOD. To evaluate the performance of the proposed method, we used a test set for validation. The experimental results show that the proposed deep learning-based method has higher accuracy and stability in AOD inversion compared to traditional methods.
Aerosols are important pollutants affecting the quality of atmospheric environment. With the development of space remote sensing technology, using satellite remote sensing to retrieve aerosol has become an important method. In order to solve the ill-posed problem of aerosol retrieval and to mine the aerosol information of satellite signals to a greater extent, this paper proposes a method based on deep confidence network to realize the aerosol retrieval of MODIS sensor. AERONET station data and satellite data in the Beijing-Tianjin-Hebei region of China were selected, and the sample dataset was constructed according to the reasonable spatio-temporal matching principle. By setting relevant parameters, the model is trained and tested, and the optimal network model is found. The aerosol measured data from independent AERONET sites were selected for accuracy evaluation, and the accuracy reached 97.78%. Compared with the traditional aerosol inversion algorithm, the proposed method achieves high precision and high efficiency of aerosol inversion, and improves the stability and spatio-temporal adaptability of aerosol inversion.
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