GF-2 Panchromatic and Multispectral Remote Sensing Image Registration Algorithm

Abstract

Image registration is an important step in remote sensing image preprocessing. The accuracy, efficiency, and automatic degree of image registration will directly affect the results of subsequent image processing and analysis. Due to the slow registration speed and low registration accuracy of SIFT algorithm for GF-2 panchromatic and multispectral remote sensing images, SIFT algorithm is improved in this article to improve the efficiency of image registration algorithm. In this article, the strategy of information entropy meshes is introduced, and feature extraction is carried out only for the regions with large information entropy, so that the running time of feature extraction can be reduced. By introducing Canny operator to eliminate the unstable edge response points, the number of feature points can be further reduced, thus the computation amount of the algorithm can be also reduced. Due to the poor registration effect of SIFT algorithm for remote sensing images, a new gradient calculation method and feature description method are used in this article to enhance the robustness of feature descriptors. In the feature matching stage, this article proposes a two-layer matching strategy, which uses mutual Euclidean distance for initial matching, then uses FSC algorithm for fine matching, and finally realizes the registration of GF-2 panchromatic and multispectral remote sensing images. Experimental results show that the proposed algorithm can obtain more correct matching point pairs when the number of extracted feature points is small. Moreover, the registration accuracy and speed are both higher than the comparison algorithm. This article uses the remote sensing images of urban scenes dominated by plains and mountainous scenes with small terrain fluctuation respectively for experiments. The algorithm in this article can achieve registration accuracy better than 0.5 pixels, and in terms of time consumption, it is only about 70% of SIFT algorithm, and the computational efficiency is better than SIFT algorithm, which can meet the actual GF-2 Pan and MSI registration tasks.