A Novel Two-Step Registration Method for Remote Sensing Images Based on Deep and Local Features

Abstract

Automatic remote sensing image registration has achieved great accomplishment. However, it is still a vital challenging problem to develop a robust and accurate registration method due to the negative effects of noise and imaging differences between images. For these images, it is difficult to guarantee the accuracy and robustness at the same time for one-step registration methods. To address this issue, we introduce an effective coarse-to-fine strategy and develop a new two-step registration method based on deep and local features in this paper. The first step is to calculate the approximate spatial relationship, which is obtained by a convolutional neural network. This step makes full use of the deep features to match and can generate stable results. For the second step, a matching strategy considering spatial relationship is applied to the local feature-based method. In addition, this step adopts more accurate features in location to adjust the results of the previous step. A variety of homologous and multimodal remote sensing images, including optical, synthetic aperture radar, and general map images, are used to evaluate the proposed method. The comparison experiments demonstrate that our method can apparently increase the correct correspondences, can improve the ratio of correct correspondences, and is highly robust and accurate.