A Novel Interest-Point-Matching Algorithm for High-Resolution Satellite Images

Interest-point matching is a key technique for image registration. It is widely used for 3-D shape reconstruction, change detection, medical image processing, computerized visioning systems, and pattern recognition. Although numerous algorithms have been developed for different applications, processing local distortion inherent in images that are captured from different viewpoints remains problematic. High-resolution satellite images are normally acquired at widely spaced intervals and typically contain local distortion due to ground relief variation. Interest-point-matching algorithms can be grouped into two broad categories: area based and feature based. Although each type has its own particular advantages in specific applications, they all face the common problem of dealing with ambiguity in smooth (low-texture) areas, such as grass, water, highway surfaces, building roofs, etc. In this paper, a new algorithm for interest-point matching of high-resolution satellite images is proposed. The conceptual basis of this algorithm is the detection of “super points,” those points which have the greatest interest strength (i.e., which represent the most prominent features) and the subsequent construction of a control network. Sufficient spatial information is then available to reduce the ambiguity and avoid false matches. We commence this paper with a brief review of current research on interest-point matching. We then introduce the proposed algorithm in detail and describe experiments with three sets of high-resolution satellite images. The experiment results show that the proposed algorithm can successfully process local distortion in high-resolution satellite images and can avoid ambiguity in matching the smooth areas. It is simple, fast, and accurate.

I. Introduction

Interest-Point matching refers to the process of matching two sets of features and finding correspondences between them. Matching interest points (sometimes called feature points or key points) is a key requirement for image registration. Image registration is widely used in photogrammetry, remote sensing, computer vision, pattern recognition, and medical image processing [6], [31]. Unfortunately, there are still many challenges with interest-point matching. The main interest-point-matching algorithms currently in use are area based or feature based. Neither type of algorithm can avoid the problem of dealing with ambiguity in smooth (low texture) areas. Feature-based algorithms face the additional problem of the effect of outliers (points with no correspondences) on the results [31].