A WTLS-based rational function model for orthorectification of remote-sensing imagery

ABSTRACT

The rational function model (RFM) is widely applied to orthorectification of aerial and satellite imagery. This article proposes a new method named Ortho-WTLS to solve the RFM in remote-sensing imagery orthorectification. Based on a weighted total least squares (WTLS) estimator, the proposed method allows one to handle coordinates of ground control points (GCPs) that contain errors and are of unequal accuracies. This situation occurs, e.g. if GCPs are automatically selected. In the proposed model, first, the relationship of two linearization methods for an RFM with errors contained in GCPs is investigated and results in a hybrid linearization. Next, based on WTLS, RFM coefficients are estimated with an iterative computation function. Finally, the performance of the Ortho-WTLS method thus obtained is investigated using simulated images and remotely sensed images by collecting GCPs with varying errors. Experimental results show that the Ortho-WTLS method achieves a more robust estimation of model parameters and a higher orthorectification accuracy when compared with standard LS-based RFM estimation. We conclude that the quality of GCPs has a large impact on the accuracy and that an increasing number of low-precision GCPs may lead to a decrease in orthorectification quality.

Acknowledgements

This research was supported in part bythe National Key Technology Research and Development Program of the Ministry of Science and Technology of China: [Grant Number 2012BAH33B01], and in part by the Knowledge Innovation Program of the Chinese Academy of Sciences: [Grant Number KZCX2-EW-QN303], and the National Natural Science Foundation of China: [Grant Numbers 41471296, 41601389]. The authors are grateful to two anonymous referees for their constructive comments, which helped to improve the quality of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported in part by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China: [Grant Number 2012BAH33B01], and in part by the Knowledge Innovation Program of the Chinese Academy of Sciences: [Grant Number KZCX2-EW-QN303], and the National Natural Science Foundation of China: [Grant Numbers 41471296, 41601389].