Paper Info
Title
Solving geometric co-registration problem of multi-spectral remote sensing imagery using SIFT-based features toward precise change detection
Abstract
This paper proposes a robust fully automated method for geometric co-registration, and an accurate statistical based change detection technique for multi-temporal high-resolution satellite imagery. The proposed algorithm is based on four main steps: First, multi-spectral scale-invariant feature transform (M-SIFT) is used to extract a set of correspondence points in a pair, or multiple pairs, of images that are taken at different times and under different circumstances, then Random Sample Consensus (RANSAC) is used to remove the outlier set. To insure an accurate matching, uniqueness constrain in the correspondence is assumed. Second, the resulting inliers matched points is used to register the given images. Third, changes in registered images are identified using statistical analysis of image differences. Finally, Markov-Gibbs Random Field (MGRF) is used to model the spatial-contextual information contained in the resulting change mask. Experiments with generated synthetic multiband images, and LANDSAT5 Images, confirm the validity of the proposed algorithm.
Year
DOI
Venue
2011
10.1007/978-3-642-24031-7_61
ISVC
Keywords
Field
DocType
change detection technique,accurate matching,resulting change mask,outlier set,markov-gibbs random field,different circumstance,sift-based feature,geometric co-registration problem,random sample consensus,precise change detection,different time,proposed algorithm,correspondence point
Computer vision,Scale-invariant feature transform,Uniqueness,Change detection,Satellite imagery,Random field,Pattern recognition,RANSAC,Computer science,Outlier,Sampling (statistics),Artificial intelligence
Conference
Citations 
PageRank 
References 
0
0.34
14
Authors
4
Name
Order
Citations
PageRank
Mostafa Abdelrahman1294.12
Asem Ali2364.69
Shireen Elhabian3142.37
Aly A. Farag42147172.03