Abstract
Image registration is an essential step in the process of image fusion, environment surveillance and change detection. Finding correct feature matches during the registration process proves to be difficult, especially for remote sensing images with large background variations (e.g., images taken pre and post an earthquake or flood). Traditional registration methods based on local intensity probably cannot maintain steady performances, as differences are significant in the same area of the corresponding images, and ground control points are not always available in many disaster images. In this paper, an automatic image registration method based on the line segments on the main shape contours (e.g., coastal lines, long roads and mountain ridges) is proposed for remote sensing images with large background variations because the main shape contours can hold relatively more invariant information. First, a line segment detector called EDLines (Edge Drawing Lines), which was proposed by Akinlar et al. in 2011, is used to extract line segments from two corresponding images, and a line validation step is performed to remove meaningless and fragmented line segments. Then, a novel line segment descriptor with a new histogram binning strategy, which is robust to global geometrical distortions, is generated for each line segment based on the geometrical relationships,including both the locations and orientations of theremaining line segments relative to it. As a result of the invariance of the main shape contours, correct line segment matches will have similar descriptors and can be obtained by cross-matching among the descriptors. Finally, a spatial consistency measure is used to remove incorrect matches, and transformation parameters between the reference and sensed images can be figured out. Experiments with images from different types of satellite datasets, such as Landsat7, QuickBird, WorldView, and so on, demonstrate that the proposed algorithm is automatic, fast (4 ms faster than the second fastest method, i.e., the rotation- and scale-invariant shape context) and can achieve a recall of 79.7%, a precision of 89.1% and a root mean square error (RMSE) of 1.0 pixels on average for remote sensing images with large background variations.
Highlights
Image registration is the process of geometrically-aligning two or more images of the same scene taken from different viewpoints, at different times or by different sensors, and the main objective of it is to find the most appropriate transformation parameters between the images [1,2,3,4]
In order to register remote sensing images with large background variations, in which most local intensity information is unreliable, we proposed an automatic image registration method based on the line segments, which is described using the information of the main shape contour
We assume that the main shape contour of an image is essentially captured by a subset of line segments originated from the edge segments
Summary
Image registration is the process of geometrically-aligning two or more images of the same scene taken from different viewpoints, at different times or by different sensors, and the main objective of it is to find the most appropriate transformation parameters between the images [1,2,3,4]. Many algorithms thathad been proposed in order to register remote sensing images with geometric and illumination differences mainly focused on the following parts: feature detection, feature description and feature matching [16,17,18,19]. When a disaster, such as a flood or earthquake, takes place, images taken pre and post the disaster should be registered rapidly in order to do the change detection [20]. Sedaghat et al [31] proposed a uniform robust scale-invariant feature matching (UR-SIFT)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
