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Reliable Line Segment Matching for Multispectral Images Guided by Intersection Matches

Accurate and robust feature matching is a critical issue in the preprocessing of multispectral image data sets. Higher order features, such as lines can provide useful matching information but are heavily affected by the unreliable detection of lines. Existing methods typically make the unrealistic assumption that end points of lines can be accurately detected across the reference and test images. To address the unreliable detection of line end points, this paper proposes mapping line intersections and then employing tentatively mapped intersections as “anchor” points to compute line descriptors. The computed line descriptors are utilized to determine whether the two lines forming an intersection are matched with the two lines forming its mapped intersection. This eliminates the reliance on the accurate detection of line end points and results in improved matching accuracy. The proposed method is tested on a large number of multispectral images containing various scenes. Experimental results show that it can effectively deal with the detection inaccuracy of end points for line matching.