Abstract
Abstract. Image registration is essential for geospatial information systems analysis, which usually involves integrating multitemporal and multispectral datasets from remote optical and radar sensors. An algorithm that deals with feature extraction, keypoint matching, outlier detection and image warping is experimented in this study. The methods currently available in the literature rely on techniques, such as the scale-invariant feature transform, between-edge cost minimization, normalized cross correlation, leasts-quares image matching, random sample consensus, iterated data snooping and thin-plate splines. Their basics are highlighted and encoded into a computer program. The test images are excerpts from digital files created by the multispectral SPOT-5 and Formosat-2 sensors, and by the panchromatic IKONOS and QuickBird sensors. Suburban areas, housing rooftops, the countryside and hilly plantations are studied. The co-registered images are displayed with block subimages in a criss-cross pattern. Besides the imagery, the registration accuracy is expressed by the root mean square error. Toward the end, this paper also includes a few opinions on issues that are believed to hinder a correct correspondence between diverse images.
Highlights
There are image feature- and area-based matching methods
The SIFT (Scale-Invariant Feature Transform) algorithm by Lowe (2004) has been famously known for its insensitivity to imaging scale and orientation changes, and to scene illumination differences, thereby allowing it to be widely accepted in disciplines like computer vision, photogrammetry and remote sensing
Based on the aligned image at a keypoint, an SIFT user sets up a vector of 128 descriptive elements
Summary
There are image feature- and area-based matching methods. A hybrid method allowing for both feature- and area-based matching techniques is considered more versatile than either method operating alone. The design of a hybrid strategy could lead to a more complex algorithm with heavy computation. Often, this is a blessed trade-off because of the increased reliability of point determination. Lowe (2004) published a scale-invariant feature transforming methodology, which allows us to generate a large number of descriptor-based keypoints. The filtered feature points possess good coordinate approximates. They may serve as initial values for the subsequent high-precision least-squares image matching
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