Compared with general scene images

unmanned aerial vehicle(UAV)images provide richer texture information

and often there are more serious problems for the one-to-many correspondence between local features and target objects. The traditional speeded-up robust features (SURF) algorithm would be inapplicable to UAV images. Therefore

an improved spatially constrained SURF method is proposed for UAV image matching and mosaicking. In the first feature point matching phase

SURF feature points are extracted from the whole base image and the blocks of the target image

respectively

a cosine-based spatial constraint relationship is built using the selected two pairs of points and imposed on the feature point dual matching process between the central block in the target image and the base image. In the second phase

the initial parameters of geometric transformation are calculated using the feature point points obtained in the first phase and used to estimate locations in the base image corresponding to points in the target image. For each feature point in the target image

point matching just need to be done within the neighborhood of the estimated locations so as to ensure matching efficiency and reliability. Meanwhile

uniformly distributed feature points are achieved with the constraint of point intensity. Finally

the obtained feature points are used for UAV image registration and mosaicking. The performance is compared with the manually selected points that are uniformly distributed. Experimental results illustrate the validity of the presented method.