Title
Slope-Restricted Multi-Scale Feature Matching for Geostationary Satellite Remote Sensing Images.
Abstract
For geostationary meteorological satellite (GSMS) remote sensing image registration, high computational cost and matching error are the two main challenging problems. To address these issues, this paper proposes a novel algorithm named slope-restricted multi-scale feature matching. In multi-scale feature matching, images are subsampled to different scales. From a small scale to a large scale, the offsets between the matched pairs are used to narrow the searching area of feature matching for the next larger scale. Thus, the feature matching is accomplished from coarse to fine, which will make the matching process more accurate and reduce errors. To enhance the matching performance, the outliers in the matched pairs are rectified by using slope-restricted rectification, which is based on local geometric similarity. Compared with other algorithms, the experimental results show that our proposed method is more accurate and efficient.
Year
DOI
Venue
2017
10.3390/rs9060576
REMOTE SENSING
Keywords
Field
DocType
remote sensing image registration,geostationary meteorological satellite (GSMS),multi-scale feature matching,slope-restricted rectification
Template matching,Computer vision,Rectification,Satellite,Pattern recognition,Remote sensing,Outlier,Feature matching,Artificial intelligence,Geology,Image registration,Geostationary orbit
Journal
Volume
Issue
Citations 
9
6
0
PageRank 
References 
Authors
0.34
19
4
Name
Order
Citations
PageRank
Dan Zeng1132.83
Lidan Wu200.34
Boyang Chen321.40
Wei Shen446426.02