Automated Georeferencing of Optical Satellite Data with Integrated Sensor Model Improvement

Abstract

The geometric processing of remotely sensed image data is one of the key issues in data interpretation, added value product generation, and multi-source data integration. Although optical satellite data can be orthorectified without the use of Ground Control Points (GCP) to absolute geometric accuracies of some meters up to several hundred meters depending on the satellite mission, there is still a need to improve the geometric accuracy by using GCP. The manual measurement of GCP is time consuming work, and leads, especially for larger data sets with hundreds of satellite images, to a cost and time ineffective workload. To overcome these shortcomings, an autonomous processing chain to georeference and orthorectify optical satellite data is proposed which uses reference data and digital elevation models to generate GCP and to improve sensor model parameters (namely for rigorous and universal sensor models) for a series of optical Earth observation satellite systems. Using a restrictive blunder removal strategy, the proposed procedure leads to high quality orthorectified products or at least to a geometrically consistent data set in terms of relative accuracy. The geometric processing chain is validated using SPOT-4 HRVIR, SPOT-5 HRG, IRS-P6 LISS III, and ALOS AVNIR-2 optical sensor data, for which a huge amount of satellite data (3,200 scenes) has been processed. Relative and absolute geometric accuracies of approximately half the pixel size (linear Root Mean Square Error) are achieved.