Combining and matching keypoints and lines on multispectral images

摘要

In this work, we proposed a method of combining lines and keypoints and matching them on multispectral images. Existing keypoint techniques usually assume images contain abundant texture information and employ hand-crafted gradient information to match keypoints. Unfortunately, mutltispectral images may lack sufficient texture information and gradient information is unable to represent well the common feature, which makes the matching of keypoints very unreliable. Lines on the contrary can be well extracted and serve as a complementary and dual image featues, but robustly describing neighboring region around lines is difficult due to inaccurate ending points and line fragmentation. In observance of the strength of lines and keypoints,this work proposes combining the keypoints and lines and matching them together. Firstly,keypoints and lines are extracted and grouped according to spatial affinity. Then we sample points in the local region for each combination and the sampled points are then used to build descriptors for each group which will be used to match the combinations of keypoints and lines. The main contribution is that the proposed method can achieve reliable feature matchings without requiring accurate end points oweing to the use of combination of keypoints and lines. The proposed method is tested on a large number of multispectral images and experimental results show that it can effectively match lines and keypoints simultaneously.