An improved geopositioning model of QuickBird high resolution satellite imagery by compensating spatial correlated errors |
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| Institution: | 1. College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, PR China;2. Center for Spatial Information Science and Sustainable Development, 1239 Siping Road, Shanghai 200092, PR China;3. Shanghai BaoSteel Industry Technological Service Co., LTD, 3521 Tongji Road, Shanghai 201900, PR China;1. ASRC InuTeq, Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198-0001, USA;2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;3. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;4. U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198-0001, USA;5. Sigma Space Corporation, VIIRS Characterization Support Team (VCST), Lanham, MD 20706, USA;1. Department of Cartographic and Land Engineering, University of Salamanca, Higher Polytechnic School of Avila, Hornos Caleros 50, 05003 Avila, Spain;2. Department of Natural Resources & Environmental Engineering, University of Vigo, School of Mining Engineering, Maxwell s/n, 36310 Vigo, Spain;1. German Aerospace Center, Oberpfaffenhofen, 82234 Weßling, Germany;2. Chair of Remote Sensing Technology, Technische Universität München, Arcisstr. 21, 80333 München, Germany;3. Photogrammetry and Remote Sensing, Technische Universität München, Arcisstr. 21, 80333 München, Germany |
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| Abstract: | A lot of studies have been done for correcting the systematic biases of high resolution satellite images (HRSI), which is a fundamental work in the geometric orientation and the geopositioning of HRSI. All the existing bias-corrected models eliminate the biases in the images by expressing the biases as a function of some deterministic parameters (i.e. shift, drift, or affine transformation models), which is indeed effective for most of the commercial high resolution satellite imagery (i.e. IKONOS, GeoEye-1, WorldView-1/2) except for QuickBird. Studies found that QuickBird is the only one that needs more than a simple shift model to absorb the strong residual systematic errors. To further improve the image geopositioning of QuickBird image, in this paper, we introduce space correlated errors (SCEs) and model them as signals in the bias-corrected rational function model (RFM) and estimate the SCEs at the ground control points (GCPs) together with the bias-corrected parameters using least squares collocation. With these estimated SCEs at GCPs, we then predict the SCEs at the unknown points according to their stochastic correlation with SCEs at the GCPs. Finally, we carry out geopositioning for these unknown points after compensating both the biases and the SCEs. The performance of our improved geopositioning model is demonstrated with a stereo pair of QuickBird cross-track images in the Shanghai urban area. The results show that the SCEs exist in HRSI and the presented geopositioning model exhibits a significant improvement, larger than 20% in both latitude and height directions and about 2.8% in longitude direction, in geopositioning accuracy compared to the common used affine transformation model (ATM), which is not taking SCEs into account. The statistical results also show that our improved geopositioning model is superior to the ATM and the second polynomial model (SPM) in both accuracy and reliability for the geopositioning of HRSI. |
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| Keywords: | QuickBird imagery Geopositioning Spatial correlated errors Least squares collocation Variance component estimation Rational function model |
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