Abstract
A new straight line matching method for aerial images is proposed in this paper. Compared to previous works, similarity constraints combining radiometric information in image and geometry attributes in object plane are employed in these methods. Firstly, initial candidate lines and the elevation values of lines projection plane are determined by corresponding points in neighborhoods of reference lines. Secondly, project reference line and candidate lines back forward onto the plane, and then similarity measure constraints are enforced to reduce the number of candidates and to determine the finial corresponding lines in a hierarchical way. Thirdly, "one-to-many" and "many-to-one" matching results are transformed into "one-to-one" by merging many lines into the new one, and the errors are eliminated simultaneously. Finally, endpoints of corresponding lines are detected by line expansion process combing with "image-object-image" mapping mode. Experimental results show that the proposed algorithm can be able to obtain reliable line matching results for aerial images.
Highlights
Reliable extraction of corresponding straight lines in overlapping images can be used for different purpose such as 3D surface reconstruction, image registration, etc (Ok et al, 2012)
The main reason is that: The extracted line in different images may be quite different due to the distortions caused by terrain relief and perspective projection; Line in one image may be occluded in other images and they may be broken into more than one piece due to image noise, light, and the deficiency of the line extraction algorithm (Zhang, 2005)
The approach in Kim et al (2012), intersecting line pairs in 2D images that are coplanar in 3D are served as matching primitive by using Line Intersection Context Feature (LICF)
Summary
Reliable extraction of corresponding straight lines in overlapping images can be used for different purpose such as 3D surface reconstruction, image registration, etc (Ok et al, 2012). Most individual line matching approaches are based on attributes similarity measure in image space. As the inclination of aerial images is bigger, the consistency of attribute in the same feature will be weaker in different images, because of this, matching result reliability is poorer based on image similarity constraints. Collins(1995) proposed feature points matching algorithm in the object space through a sweep plane technique. Similarity to the Collins’ algorithm, a space plane is used to constraint line matching. Different to sweeping plane from Zmax to Zmin, we determine the initial Z-position of the plane by using the corresponding points in the neighborhood of the matching straight line. Backproject reference line and candidate lines onto the plane Z = zi, similarity measure constraints are.
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