An automatic parameter selection procedure for pushbroom sensor models on imaging satellites

For the rigorous, physical modeling of spaceborne, pushbroom imaging sensors, over-parameterization and the resulting dependencies among sensor model parameters are a continuing issue causing instability and ambiguity during parameter estimation. Traditionally, this problem has been tackled by a fixed subset approach or by using a priori stochastic constraints, which generally require the user's intuition or intervention but with no guarantee that an optimal solution is obtained. An efficient and automated procedure to find an optimal parameter subset, that is independent and meets accuracy requirements, has been developed and tested using six images from six representative sensors. The experimental results show a stable performance of the developed procedure which results in a quality subset by the evaluation criteria and tries to minimize the checkpoint misclosure (i.e., LOOCV RMSE) so that the resulting subset can be considered optimum. Therefore, the proposed procedure can be beneficial to the users and sensor model developers by providing an optimal and subjective solution to the well known over-parameterization problem in satellite sensor model.