Method for Maximizing the Number of Detected Keypoints on Homogeneous Underlying Surfaces

Abstract

RGB images are commonly used as input data for image processing systems, but, the informative value of RGB visualization can be insufficient, especially in situations where the area to be processed is homogenous and a mixture of terrain objects fall on a single pixel of the image. Spectral data is essential for effective processing homogenous areas of the underlying surface. In this paper, the authors demonstrate the advantages of using the spectral indices visualization as an input for keypoint detection and matching tasks. Position shift detection problems require process of finding key points in the image. However, in such tasks, it is not always possible to find a sufficient number of keypoints using only RGB data. This problem appears when you trying to process homogeneous types of surfaces such as forests, deserts, and water spaces. The Main task of this paper was to develop a method based on a combination of spectral channels to achieve an increase in the number of keypoints detected on a pre-defined homogeneous underlying surface. The results of the conducted study showed that the number of detected keypoints on average increased 1.44 times in the calculated index images compared to RGB images and, in turn, resulted in an average 1.592-fold increase in the number of key point matches in the current and previous images.