6s(Second Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer model is one of the atmospheric correction algorithms based on the atmospheric radiative transmission model. It is widely used because of its high correction accuracy. Meanwhile, it is criticized for the complexity of the parameters and the efficiency of the correction process. 6S model needs to establish a look-up table based on the geometric conditions and aerosol conditions which directly determines the accuracy of the atmospheric correction. This paper analyzes the limitations of traditional look-up table method and uses artificial intelligence algorithms such as the support vector regression(SVR) algorithm and the back propagation (BP) algorithm to instead the traditional look-up table method. The experiments’ results show that the output value and predictive value fit well. Both are better than the traditional linear interpolation performance results, and the BP algorithms performs better, which verifies the feasibility of BP neural network algorithms prediction model instead of linear interpolation method for table lookup. Finally, this paper takes Landsat-8 data as an example, uses the method proposed in this article to perform atmospheric correction, and compares the FLAASH model correction results. The visual performance results of the two are roughly the same.
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