Abstract
Abstract. Image matching is a key technology for extraction of dense point cloud and 3D terrain information using satellite/aerial imagery. In image matching using brightness values of pixels, the size of search window is an important factor for determining the matching performance. In this study, we perform matching using multi-dimensional search windows applicable to area-based matching and compare the performance. Also, the search window is reconfigured by using the linear information existing on the image, and the matching is tried. Comparing the fixed search window and the multi-window matching results, it was confirmed that the multiple windows under the same conditions show relatively high accuracy. We can also see that the method of applying the line element has slightly better accuracy. As a result of applying the line element extraction technique, a large number of pixels are not extracted compared with the total image pixel amount. There was no significant difference in the results of visual analysis. However, we have confirmed that this technique has contributed to improving accuracy.
Highlights
The 3D information obtained from the image is extracted to DSM (Digital Surface Model) or point cloud, and it can be used as an image map or a 3D object model
Image matching is a key technology for extraction of dense point cloud and 3D terrain information using satellite/aerial imagery
In image matching using brightness values of pixels, the size of search window is an important factor for determining the matching performance
Summary
The 3D information obtained from the image is extracted to DSM (Digital Surface Model) or point cloud, and it can be used as an image map or a 3D object model. Area-based matching uses the brightness values around one point and uses the correlation coefficient or entropy of these points. This technique has advantages of fast computation, but it has a disadvantage that accuracy is unstable. SGM uses mutual information based on the brightness value information This technique cannot ignore area-based pixel information in that it computes the matching cost around the pixels. In image matching using brightness values of pixels, the size of search window is an important factor for determining the matching performance. 2016 reported that dense image matching can be achieved by using only local area-based matching through previous studies, and proposed a technique using multi-dimensional search window and global optimization method. The search window is reconfigured by using the linear information existing in the image, and the matching is tried
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