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Remote Sens. 2017, 9(6), 587;

Geometry-Based Global Alignment for GSMS Remote Sensing Images

Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200070, China
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100195, China
The 16th Institute, China Aerospace Science and Technology Corporation, Shaanxi 710100, China
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez and Prasad S. Thenkabail
Received: 11 March 2017 / Revised: 11 May 2017 / Accepted: 7 June 2017 / Published: 10 June 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
PDF [1069 KB, uploaded 10 June 2017]


Alignment of latitude and longitude for all pixels is important for geo-stationary meteorological satellite (GSMS) images. To align landmarks and non-landmarks in the GSMS images, we propose a geometry-based global alignment method. Firstly, the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG) database and GSMS images are expressed as feature maps by geometric coding. According to the geometric and gradient similarity of feature maps, initial feature matching is obtained. Then, neighborhood spatial consistency based local geometric refinement algorithm is utilized to remove outliers. Since the earth is not a standard sphere, polynomial fitting models are used to describe the global relationship between latitude, longitude and coordinates for all pixels in the GSMS images. Finally, with registered landmarks and polynomial fitting models, the latitude and longitude of each pixel in the GSMS images can be calculated. Experimental results show that the proposed method globally align the GSMS images with high accuracy, recall and significantly low computation complexity. View Full-Text
Keywords: image alignment; feature matching; geostationary satellite remote sensing image; GSHHG database image alignment; feature matching; geostationary satellite remote sensing image; GSHHG database

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Zeng, D.; Fang, R.; Ge, S.; Li, S.; Zhang, Z. Geometry-Based Global Alignment for GSMS Remote Sensing Images. Remote Sens. 2017, 9, 587.

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