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Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery Based on Statistical Learning Theory

by Niangang Jiao 1,2,3,*, Feng Wang 1,2, Hongjian You 1,2,3, Mudan Yang 1,2,3 and Xinghui Yao 1,2,3
1
Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(6), 1701; https://doi.org/10.3390/s18061701
Received: 22 April 2018 / Revised: 23 May 2018 / Accepted: 23 May 2018 / Published: 24 May 2018
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
With the increasing demand for high-resolution remote sensing images for mapping and monitoring the Earth’s environment, geometric positioning accuracy improvement plays a significant role in the image preprocessing step. Based on the statistical learning theory, we propose a new method to improve the geometric positioning accuracy without ground control points (GCPs). Multi-temporal images from the ZY-3 satellite are tested and the bias-compensated rational function model (RFM) is applied as the block adjustment model in our experiment. An easy and stable weight strategy and the fast iterative shrinkage-thresholding (FIST) algorithm which is widely used in the field of compressive sensing are improved and utilized to define the normal equation matrix and solve it. Then, the residual errors after traditional block adjustment are acquired and tested with the newly proposed inherent error compensation model based on statistical learning theory. The final results indicate that the geometric positioning accuracy of ZY-3 satellite imagery can be improved greatly with our proposed method. View Full-Text
Keywords: statistical learning theory; multi-temporal; block adjustment; rational function model (RFM); improved fast iterative shrinkage-thresholding algorithm; inherent error compensation model statistical learning theory; multi-temporal; block adjustment; rational function model (RFM); improved fast iterative shrinkage-thresholding algorithm; inherent error compensation model
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Jiao, N.; Wang, F.; You, H.; Yang, M.; Yao, X. Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery Based on Statistical Learning Theory. Sensors 2018, 18, 1701.

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