# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 4 Conclusions and Discussion  
The present study proposes a novel approach, namely, IFF-NET network, for effectively categorizing multimodal remote sensing data by leveraging the fusion of irregular features. Feature extraction is accomplished by employing residual blocks that have weights shared among them, while also ensuring the presence of an individual batch normalization layer. In the sample data training phase, the BN layer is used to calculate and determine the redundancy of the current channel. In cases where the judgment factor falls below a certain threshold, the existing channel information becomes repetitive and is substituted with an alternative channel. Sparse restrictions are applied to select judgement factors in order to eliminate unnecessary channels and enhance generalization capabilities. Furthermore, a module for normalizing and calibrating features is developed to leverage the spatial correlation of multimodal features in order to improve their discriminative power. The IFF-NET network introduced in this study presents superior benefits, improved classification outcomes, and enhanced performance in classification tasks. The effectiveness and efficiency of the IFF-Net network in classifying multimodal remote sensing data are also demonstrated.  
Although the proposed IFF-Net classification model obtains a certain classification accuracy, there are still some problems that need to be solved by future later research, specifically, the following aspects: (1) The model is trained with a limited number of labeled samples, which affects the robustness of the model. In our future research work, we will work on pruning, data augmentation and regularization methods to improve model robustness in the small sample case [41]. (2) The model sacrifices the classification accuracy to a certain extent to reduce the pressure of sample data, and more efficient fusion models can be designed to balance this problem in future research. (3) The model still needs to rely on the labeled data for supervised classification in the process of classification, and future research can try to guarantee the classification accuracy of the labeled data. In the future, we can try to introduce semi-supervised or unsupervised classification methods under the premise of guaranteeing the classification accuracy. (4) The complexity of the model means it needs to be further improved in terms of practicality and application ability, and the generalization ability of the model needs to be further improved. Future research needs to be verified and improved by more datasets to adapt to more complex scene classification.  
Conceptualization, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]); formal analysis, [PERSON]; data preprocessing, [PERSON] ([PERSON]); writing--original draft preparation [PERSON]. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); visualization, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). The manuscript was corrected and improved by all authors. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Collaborative Education Research Project of the Ministry of Education (Fund No: 230903175063441).  
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The authors declare no conflict of interest.