A Novel Keypoint Detector Combining Corners and Blobs for Remote Sensing Image Registration

Keypoint detection is a crucial step for feature-based image registration. The traditional detectors only extract one type of keypoint such as a corner or a blob, which is not quite beneficial to image registration. Accordingly, this letter presents a novel keypoint detector that aims to simultaneously extract corners and blobs. The proposed detector is named as Harris-Difference of Gaussian (DoG), which combines the advantages of the Harris–Laplace corner detector and the DoG blob detector. In the definition of Harris-DoG, we first build an image scale space and extract the corners by using the multiscale Harris detector. Then, these corners are screened by an automatic scale selection technique based on their DoG responses. This can make the corners robust to scale changes. Meanwhile, DoG also is used to detect the blobs in the image scale space by a nonmaxima suppression scheme. Finally, the scale invariant feature transform (SIFT) descriptors are computed for the detected corners and blobs, and they are applied together for image registration. The proposed Harris-DoG has been tested by using three pairs of multisensor remote sensing images. The experimental results show that Harris-DoG can effectively increase the number of correct matches and improve the registration accuracy compared with the state-of-the-art keypoint detectors.