A remote sensing image matching network combining a large selective kernel-enhanced convolution module

doi:10.6046/zrzyyg.2024365

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Abstract

Extracting information on various surface features from remote sensing images requires varying contextual data. To address this issue, this study proposed a new feature point matching method that integrated a large selective kernel-enhanced convolutional module. In this method, based on the ResNet34 network, a large selective kernel-enhanced convolutional module was embedded for dynamic feature extraction of different surface feature targets. Then, the initial dense matching was obtained using a sparse neighborhood consensus network. Meanwhile, geometric and motion consistency constraints were introduced to conduct the guided diffusion of matching points. Consequently, optimized matching results were achieved. This method yielded a PCK (α=0.05) accuracy of 0.89 on the Google Earth dataset, which increased by 7.22%, 5.95%, 2.30%, 4.71%, 7.22%, and 9.88%, respectively, compared to the SuperPoint, R2D2, NCNet, Sparse-NCNet, LoFTR, and Two-Stream networks. Additionally, it exhibited a high generalization ability on the Hpatches dataset. These results corroborate the effectiveness of the proposed method.

Keywords feature point matching large selective kernel-enhanced convolutional structure spatial reconstruction geometric consistency motion consistency

ZTFLH:	TP751
	TP79

Issue Date: 31 December 2025

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	Yuxi DENG
	Jiatian LI
	Jiayin LIU
	Xin LUO
	Tao YANG

Cite this article:

Yuxi DENG,Jiatian LI,Jiayin LIU, et al. A remote sensing image matching network combining a large selective kernel-enhanced convolution module[J]. Remote Sensing for Natural Resources, 2025, 37(6): 138-147.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024365 OR https://www.gtzyyg.com/EN/Y2025/V37/I6/138

Fig.1 Remote sensing image feature point matching network combining enhanced large selective kernel convolution module

Fig.2 Large selective Kernel convolution structure

Fig.3 Spatial reconstruction unit

Fig.4 Enhanced large selective Kernel convolution module

Tab.1 Google Earth datasets Precision comparison

Fig.5 The visualization results of the contrastive network on the Google Earth dataset

Fig.6 The visualization results of the network in this paper

Tab.2 WHU-RS19 datasets Precision comparison

Fig.7 The visualization results of the contrastive network on the WHU-RS19 dataset

Fig.8 The visualization results of the network in this paper

Tab.3 Hpatches datasets MMA Precision comparison

Tab.4 Results of ablation experiments of based enhanced large selective Kernel convolution module

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