High-resolution image registration based on improved SURF detector and localized GTM

High-resolution image registration is an important task in remote sensing image processing. In this paper, an automatic and robust local feature-based image registration approach is proposed for high-resolution remote sensing images. The proposed method consists of four main steps. In the first step, an integrated local feature-based matching method based on an improved speeded-up robust features (SURF) detector and an adaptive binning scale-invariant feature transform (AB-SIFT) descriptor is developed for fast, dense and robust tie-point extraction. In the second step, a localized graph transformation matching (LGTM) method is developed for reliable mismatch elimination. In the third step, an advanced oriented least square matching (OLSM) method is applied to improve the positional accuracy of the refined tie-points. Finally, the input image is warped using an appropriate transformation model. To investigate the impact of the transformation function, the capability of some models, including, polynomials of degrees 2 to 4, piecewise linear (PL), weighted mean (WM) and multiquadric (MQ) are compared. The proposed method has been evaluated with five pairs of high-resolution remote sensing images from IRS-P5, SPOT 5, SPOT 6, IKONOS, Geoeye, Quickbird, and Worldview sensors, and the registration results demonstrate its robustness and capability. The MATLAB code of the proposed method can be downloaded from .