DocumentCode
737999
Title
Method for Large-Area Satellite Image Quality Enhancement With Local Aerial Images Based on Non-Target Multi-Point Calibration
Author
Guorui Ma ; Qianqian Wei ; Haigang Sui ; Qianqing Qin
Author_Institution
State Lab. for Inf. Eng. in Surveying, Mapping & Remote Sensing, Wuhan Univ., Wuhan, China
Volume
6
Issue
5
fYear
2013
Firstpage
2174
Lastpage
2183
Abstract
This paper designed a non-target multi-point calibration method for the quality enhancement of large-area satellite images by using local aerial images. Satellite images are more sensitive to atmospheric effects compared with aerial images. Atmospheric effects on aerial images are even negligible in fine weather. Given that aerial remote sensing has high spatial resolution and geometric fidelity, more spatial details can be recorded in aerial images. However, the scan bandwidth of aerial images is limited compared with that of satellite images. Thus, taking high-quality aerial images of a neighborhood as reference can provide prior knowledge for point spread function (PSF) estimation and for the quality enhancement of large-area satellite images. The least square method and interpolation are used for the PSF estimation of spatial variation, and then total variation minimization is used for recovery. The results show that the designed method can effectively enhance the quality of large-area satellite images.
Keywords
geophysical image processing; image enhancement; image restoration; remote sensing; PSF estimation; Terms-Image restoration; aerial remote sensing; atmospheric effects; geometric fidelity; large-area satellite image quality enhancement; local aerial images; nontarget multipoint calibration method; point spread function; Image restoration; least squares; space-variant point-spread functions; total variation;
fLanguage
English
Journal_Title
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Publisher
ieee
ISSN
1939-1404
Type
jour
DOI
10.1109/JSTARS.2013.2250920
Filename
6494341
Link To Document