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Neuro-variational inversion of ocean color imagery

机译:海洋彩色图像的神经变分反演

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This paper presents a neuro-variational method to invert satellite ocean color signal. The method is based on a combination of neural networks and classical variational inversion. The radiative transfer equations are modeled by neural networks whose input are the oceanic and atmospheric parameters and output the top of the atmosphere reflectance at several wavelengths. The procedure consists in minimizing a quadratic cost function which is the distance between the satellite observed reflectance and the neural network computed reflectance, the control parameters being the oceanic and atmospheric parameters. The method allows us to retrieve atmospheric and oceanic parameters. We present a feasibility experiment. We show we can retrieve Chl-a with an error of 19.7% if we can obtain a perfect knowledge of three atmospheric parameters. Finally, an inversion of one SeaWiFS image is presented. The Chl-a give coherent spatial structures.
机译:本文提出了一种神经变异方法来反演卫星海洋颜色信号。该方法基于神经网络和经典变分反演的组合。辐射传递方程由神经网络建模,该神经网络的输入是海洋和大气参数,并在多个波长下输出大气反射率的顶部。该程序包括最小化二次成本函数,该函数是卫星观测到的反射率与神经网络计算的反射率之间的距离,控制参数是海洋和大气参数。该方法使我们能够检索大气和海洋参数。我们提出一个可行性实验。我们证明,如果我们能够完全了解三个大气参数,则可以检索到Chl-a,误差为19.7%。最后,给出了一个SeaWiFS映像的反演。 Chl-a提供了连贯的空间结构。

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