An extension of augmented Lagrange multiplier method for remote sensing inversion

Remote sensing inversions are often typical constrained optimization problems. Based on the analysis of the current algorithms' applicability in the field of remote sensing, we propose an extended augmented Lagrange multiplier method with improved rate-of-convergence and less ill-posed problem. This method is developed on a new concept of penalty matrix. Through detailed simulation and inversion, it is clear from the statistical analysis that the rate-of-convergence has been improved of about 30 percent compared with the original penalty factor based method but with similar accuracies. Furthermore, it is also found that our extended method is resistant to ill-posed problems.