Abstract
Remotely sensed images are the main source for a variety of mapping and change-detection applications. Images from different satellites are employed in several of these applications. However, each type of these images has a different resolution and orientation. Hence, they need to be co-registered before any meaningful use. The first step in the registration process is to find conjugate points between the images. This paper presents a modified method of the Scott and Longuet-Higgins approach to find conjugate points between different remotely sensed images. In such an algorithm, initially, corner points are automatically extracted in two images, and for each pair of points, a cost value is computed. The cost of corresponding any two points is computed using two-dimensional transformation models and pixel intensities. The cost values are then used to fill a cost matrix, and its singular value decomposition is used to find corresponding points. The algorithm is tested on three pairs of satellite images with different resolutions and orientations. The results show that the approach presented here succeeded in finding 93% of conjugate points between different pairs of satellite images using only the image coordinates through the eight-parameter transformation model. Moreover, the results show that including the image intensities in the matching procedure does change the results significantly.