# Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network
## 5 Conclusions  
In this paper, we propose a novel approach that combines PCA-based noise adaptiveness with a learnable matching network to address the challenge of matching remotely sensed multimodal images. Our method focuses on enhancing the noise adaptiveness of structural features and providing a robust trainable measurement of similarity while regressing transform parameters.  
By integrating the learnable matching network with PCA-enhanced structure features, our proposed method effectively handles the complex radiometric variations that exist  
Figure 11: Matching results of T5–T16 template sizes: (**a**) \(64\times 64\); (**b**) \(96\times 96\); (**c**) \(128\times 128\); (**d**) \(160\times 160\).  
among remotely sensed multimodal images. This adaptability is crucial in achieving robust image matching. As demonstrated in the experiments, the PCA-LMN approach achieves the best matching performance among all the methods evaluated. The average CMR achieved by PCA-LMN is 91.69%, which surpasses the CMR of CFOG, the best traditional method, by 10.63%.  
The ablation study in Section 4.3 further revealed that the improved performance of PCA-LMN can be attributed to two main factors. Firstly, PCA-LMN benefits from the PCA orientation enhancement, which enables accurate capture of structural features even in the presence of significant noise distortions. This enhancement plays a vital role in matching multimodal images with challenging noise distortions. Secondly, LMN is trained to adapt to gradient reversal caused by radiometric differences among remotely sensed multimodal images. This adaptability allows for more precise measurements and better handling of radiometric variations compared to traditional methods.  
Furthermore, our method offers the advantage of not requiring the training of a deep convolutional neural network for feature extraction. This makes it easy to train and deploy, and it can achieve stable performance even with small training datasets. The testing results in Section 4.3 show that PCA-LMN does have a significant advantage over DHE (which employs deep convolutional neural network for feature extraction) when the training dataset is small.  
While our proposed method demonstrates superior performance, it is important to note that it lacks the ability to handle rotation and scale variations between images. As a prerequisite for accurate matching, it is necessary to correct the template's direction and scale to align them with the base image approximately. Failure to do so can result in changes to directional features due to rotation and alterations to the scale of extracted features caused by image scaling.  
Conceptualization, J.D. and S.H.; methodology, S.H. and J.D.; software, J.D., S.H. and H.L.; validation, S.H., J.D. and H.L.; formal analysis, X.L.; investigation, X.L.; resources, J.D.; data curation, X.L.; writing--original draft preparation, J.D., S.H. and X.L.; writing--review and editing, J.D. and X.L.; visualization, H.L.; supervision, J.D. and S.H.; project administration, H.L.; funding acquisition, S.H. and H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62203163; and in part by the Scientific Research Foundation of Hunan Provincial Department of Education under Grant 21B0661.  
Not applicable.  
The are no new data is created.  
The authors declare no conflict of interest.