A Generic Method for RPC Refinement Using Ground Control Information
Geometric sensor models are used in image processing to model the relationship between object space and image space and to transform image data to conform to a map projection. An Rational Polynomial Coefficient (RCP) is a generic sensor model that can be used to transform images
from a variety of different high resolution satellite and airborne remote sensing systems. To date, numerous researchers have published papers about RPC refinement, aimed at improving the accuracy of the results. So far, the Bias Compensation method is the one that is the most accepted and
widely used, but this method has rigorous conditions that limit its use; namely, it can only be used to improve the RPC of images obtained from cameras with a narrow field of view and small attitude errors, such as those used on Ikonos or QuickBird satellites. In many cases, these rigorous
conditions may not be satisfied (e.g., cameras with a wide field of view and some satellites with large ephemeris and attitude errors). Therefore, a more robust method that can be used to refine the RPC under a wider range of conditions is desirable. In this paper, a generic method for RPC
refinement is proposed. The method first restores the sensor’s pseudo position and attitude, then adjusts these parameters using ground control points. Finally a new RPC is generated based on the sensor’s adjusted position and attitude. We commence our paper with a review of the
previous ten years of research directed toward RPC refinement, and compare the characteristics of different refinement methods that have been proposed to date. We then present a methodology for a proposed generic method for RPC refinement and describe the results of two sets of experiments
that compare the proposed Generic method with the Bias Compensation method. The results confirm that the Bias Compensation method works well only when the aforementioned rigorous conditions are met. The accuracy of the RPC refined by the Bias Compensation method decreased rapidly with the
sensor’s position error and attitude error.
In contrast to this, the Generic method proposed in this paper was found to yield highly accurate results under a variety of different sensor positions and attitudes.
In contrast to this, the Generic method proposed in this paper was found to yield highly accurate results under a variety of different sensor positions and attitudes.
Document Type: Research Article
Publication date: 01 September 2009
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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