Abstract
Numerous countries have established their own Earth observing systems (EOSs) for global change research. Data acquisition efforts are generally only concerned with the completion of the mission regardless of the potential to expand into other areas, which reduces the application effectiveness of Earth observation data. This paper explores the cartographic possibility of images being not initially intended for surveying and mapping, and a novel method is proposed to improve the geometric performance. First, the rigorous sensor model (RSM) is recovered from the rational function model (RFM), and then the system errors of the non-cartographic satellite’s imagery are compensated by using the conventional geometric calibration method based on RSM; finally, a new and improved RFM is generated. The advantage of the method over traditional ones is that it divides the errors into static errors and non-static errors for each image during the improvement process. Experiments using images collected with the Gaofen-1 (GF-1) wide-field view (WFV) camera demonstrate that the orientation accuracy of the proposed method is within 1 pixel for both calibration and validation images, and the obvious high-order system errors are eliminated. Moreover, a block adjustment test shows that the vertical accuracy is improved from 21 m to 11 m with four ground control points (GCPs) after compensation, which can fulfill requirements for 1:100,000 stereo mapping in mountainous areas. Generally, the proposed method can effectively improve the geometric potential for images captured by non-cartographic satellite.
Highlights
Numerous countries have established their own Earth observing systems (EOSs)
The rigorous sensor model (RSM) is recovered from the rational function model (RFM) and the system errors of non-cartographic satellite’s imagery can be compensated for by using the conventional geometric calibration method based on the RSM; improved RFM is described
Unlike conventional Bias compensation methods (BCMs) methods, this method compensates for the system errors based on their cause rather than by building a compensation model according to the fit or to approximate the residuals
Summary
Numerous countries have established their own Earth observing systems (EOSs). For example, China has been working on the establishment of the meteorological Fengyun (FY) satellite series, oceanic Haiyang (HY) satellite series, Earth resource Ziyuan (ZY) satellite series [1,2], Environment and Disaster Monitoring Huanjing (HJ) satellite series, and China High-resolution Earth Observation System (CHEOS) [3,4]. The United States has developed an EOS plan [5], Earth Science Business Plan (ESE), and Integrated Earth Observation System (IEOS) [6], and it has launched numerous satellites including Landsat, Terra, Earth Observing-1 (EO-1), and other satellites [7]. Other countries such as Russia [8], Japan [9], Canada [10] and India [11] have put forward corresponding Earth observation plans. These data have laid a solid foundation for global change research, and presently, there are various proposed methods and product specifications for such data
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