ADRNet: Affine and Deformable Registration Networks for Multimodal Remote Sensing Images

Abstract

Multi-modal remote sensing images registration ensures the consistency of the spatial positions for different images. It can provide the accurate geographic information and supports the fusion of multi-source data for geospatial analyses and applications. Rigid registration method shows high performance in dealing with large-scale deformation, but it is difficult to achieve high-precision image registration. In contrast, non-rigid registration method is suitable for processing local differences, but cannot effectively deal with large-scale deformation differences. Therefore, the combination of rigid and non-rigid registration methods becomes a necessary strategy to address such issues. In this paper, we propose a novel ADRNet method for multi-modal remote sensing images registration. The proposed ADRNet method contains three main modules: affine registration module, deformable registration module, and spatial transformer module that integrates the affine and deformable transformation parameters to obtain the final aligned images. Meanwhile, we design a new feature enhancement module and an attention module with dilated convolutions which have different dilation rates, which are used to alleviate the limitations imposed by receptive fields in the convolution operation. Moreover, we propose a specific symmetric loss function to optimize the whole network from the perspective of inverse consistency. To assess the efficiency and performance of the network, we extend the experimental data, ranging from cross-modal images in a conventional viewpoint to cross-modal images in a remote sensing viewpoint. The experimental results show that our method exhibits excellent performance for the images with different viewpoints and deformation scales. The relevant code will be released at: https://github.com/Ahuer-Lei/ADRNet.

Topics & Concepts