Next Article in Journal
Accuracy Assessment of LiDAR-Derived Digital Elevation Models Based on Approximation Theory
Previous Article in Journal
Potential of VIS-NIR-SWIR Spectroscopy from the Chinese Soil Spectral Library for Assessment of Nitrogen Fertilization Rates in the Paddy-Rice Region, China
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(6), 7044-7061;

An ASIFT-Based Local Registration Method for Satellite Imagery

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, 92 Weijin Road, Tianjin 300072, China
Computer Vision Group, School of Systems Engineering, University of Reading, Berkshire RG6 6AY, UK
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 20 March 2015 / Revised: 12 May 2015 / Accepted: 22 May 2015 / Published: 29 May 2015
Full-Text   |   PDF [3271 KB, uploaded 1 June 2015]   |  


Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensing applications in which ground control points (GCPs) are not always available, such as in selecting a landing zone on an outer space planet. In this paper, a framework for localization in image registration is developed. It strengthened the local registration accuracy from two aspects: less reprojection error and better feature point distribution. Affine scale-invariant feature transform (ASIFT) was used for acquiring feature points and correspondences on the input images. Then, a homography matrix was estimated as the transformation model by an improved random sample consensus (IM-RANSAC) algorithm. In order to identify a registration region with a better spatial distribution of feature points, the Euclidean distance between the feature points is applied (named the S criterion). Finally, the parameters of the homography matrix were optimized by the Levenberg–Marquardt (LM) algorithm with selective feature points from the chosen registration region. In the experiment section, the Chang’E-2 satellite remote sensing imagery was used for evaluating the performance of the proposed method. The experiment result demonstrates that the proposed method can automatically locate a specific region with high registration accuracy between input images by achieving lower root mean square error (RMSE) and better distribution of feature points. View Full-Text
Keywords: satellite remote sensing; local image registration; image mosaic; ASIFT; RANSAC satellite remote sensing; local image registration; image mosaic; ASIFT; RANSAC

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Wang, X.; Li, Y.; Wei, H.; Liu, F. An ASIFT-Based Local Registration Method for Satellite Imagery. Remote Sens. 2015, 7, 7044-7061.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top