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Combined adjustment of multi-resolution satellite imagery for improved geo-positioning accuracy

发布日期:2016-11-30 23:12:17 阅读次数:[1651]次 作者:

核心提示:来源出版物: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

作者: Tang, SJ (Tang, Shengjun); Wu, B (Wu, Bo); Zhu, Q (Zhu, Qing) 
来源出版物: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING  卷: 114  页: 125-136  DOI: 10.1016/j.isprsjprs.2016.02.003  出版年: APR 2016
摘要: Due to the widespread availability of satellite imagery nowadays, it is common for regions to be covered by satellite imagery from multiple sources with multiple resolutions. This paper presents a combined adjustment approach to integrate multi-source multi-resolution satellite imagery for improved geo-positioning accuracy without the use of ground control points (GCPs). Instead of using all the rational polynomial coefficients (RPCs) of images for processing, only those dominating the geo-positioning accuracy are used in the combined adjustment. They, together with tie points identified in the images, are used as observations in the adjustment model. Proper weights are determined for each observation, and ridge parameters are determined for better convergence of the adjustment solution. The outputs from the combined adjustment are the improved dominating RPCs of images, from which improved geo-positioning accuracy can be obtained. Experiments using ZY-3, SPOT-7 and Pleiades-1 imagery in Hong Kong, and Cartosat-1 and Worldview-1 imagery in Catalonia, Spain demonstrate that the proposed method is able to effectively improve the geo-positioning accuracy of satellite images. The combined adjustment approach offers an alternative method to improve geo-positioning accuracy of satellite images. The approach enables the integration of multi-source and multi-resolution satellite imagery for generating more precise and consistent 3D spatial information, which permits the comparative and synergistic use of multi-resolution satellite images from multiple sources. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.


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