Title: Aerial remote sensing image registration based on dense residual network of asymmetric convolution

Authors: Ying Chen; Wencheng Zhang; Wei Wang; Jiahao Wang; Xianjing Li; Qi Zhang; Yanjiao Shi

Addresses: School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China ' School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China ' School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China ' School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China ' School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China ' School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China ' School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China

Abstract: The existing image registration frameworks pay less attention to important local feature information and part of global feature information, resulting in low registration accuracy. However, asymmetric convolution and dense connection can pay more attention to the key information and shallow information of the image. Therefore, this paper proposes a novel feature extraction module to improve the feature extraction ability and registration accuracy of the model. Asymmetric convolution and dense connection are used to improve the residual structure to focus on both local and global information in the feature extraction stage. In the feature matching stage, bidirectional matching is used to alleviate asymmetric matching results by fusing two outcomes. Furthermore, a secondary affine transformation is proposed to estimate the real transformation between two images adequately. In contrast with several popular algorithms, the proposed method has a better registration effect on two public datasets, which has practical significance.

Keywords: remote sensing image registration; residual network; asymmetric convolution; dense connection; transfer learning; regularisation; affine transformation.

DOI: 10.1504/IJCSE.2024.139625

International Journal of Computational Science and Engineering, 2024 Vol.27 No.4, pp.393 - 405

Received: 25 Jan 2022
Received in revised form: 09 May 2022
Accepted: 10 May 2022
Published online: 05 Jul 2024 *

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