The maximum entropy algorithm for the determination of the Tikhonov regularization parameter in quantitative remote sensing inversion

Remote sensing inversion problem is always ill-posed. However, regularization aims at turning the ill-posed problems into certainty. In this paper, taking the linear kernel-driven model as an example, we put forward the maximum entropy algorithm based on information theory to determine the Tikhonov regularization parameter. Then, analyse and compare it with other mature methods. Result shows that the maximum entropy algorithm has advantages when the variance of the observations´ noise is small or uncertainty of the prior knowledge is not too large.