# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 3 Results
### Results and Analysis on Augsburg Data  
(1) Quantitative comparison: Table 7 presents the quantitative outcomes of various multimodal techniques on three widely employed metrics, namely, OA, AA, and \(\kappa\). In general, the classification accuracy of the deep-learning-based classification methods is better than that of other methods, such as Two-branch CNN and the IFF-Net method proposed in this paper. The classification accuracy of multimodal data fusion is to some extent higher than that of single-modal data, e.g., the classification accuracy of support vector machine (SVM) classifiers is slightly higher for HSI + SAR for the individual analysis of HSI or SAR. The classification accuracies achieved by traditional subspace and morphological methods exhibit a slight superiority over those obtained through SVM and \begin{table}
\begin{tabular}{c c c} \hline \hline \(\lambda\) & **Berlin (OA\%)** & **Augsburg (OA\%)** \\ \hline
1 & 65.71 & 88.96 \\ \hline
0.5 & 68.86 & 88.48 \\ \hline
0.1 & 69.44 & 89.73 \\ \hline
0.05 & 71.02 & 90.52 \\ \hline
0.01 & 69.59 & 90.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The relationship between OA and the regularization factor \(\lambda\) is described. \begin{table}
\begin{tabular}{c c c} \hline \hline \(n\) & **Berlin (OA\%)** & **Augsburg (OA\%)** \\ \hline
1 & 67.31 & 88.46 \\ \hline
2 & 71.02 & 90.52 \\ \hline
3 & 68.84 & 89.25 \\ \hline
4 & 69.10 & 89.32 \\ \hline
5 & 66.79 & 87.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of OA and residual blocks. CapsNet approaches with simple data overlays. Among the semi-supervised methods, the LeMA method fully considers both labeled and unlabeled samples, maps different modalities of the data through a common subspace, and achieves higher classification accuracy, with more than 1% improvement in OA compared to SVM classifiers. The OA value is enhanced by a minimum of 1.6% through the utilization of deep-learning-based classification methods, including Two-branch CNN and the proposed IFF-Net method. Based on the experimental results presented in Table 4, it is evident that SVM exhibits a tendency to generate numerous noise regions by simply stacking the data, resulting in slightly inferior classification accuracy methods. The CapsNet method employs feature stacking in the feature fusion stage; however, it fails to fully integrate the spatial and spectral information. Consequently, the classification OA value closely resembles that of SVM and LeMA with minimal disparity. Two-branch CNN and IFF-Net excel at extracting highly distinctive feature representations and effectively capturing the inherent correlations among diverse modalities. This leads to better classification results using IFF-Net and Two-branch CNN. The OA of the S2 FL method is better than the CapsNet method, but the classification accuracy of the single category is not outstanding. The OA of Two-branch CNN is better than those of the S2 FL and CapsNet methods, and it has the highest accuracy and the best performance on category C3 and category C7. The well-designed feature fusion module improves the discrimination of the fused information. Compared with other methods, the OA value of the IFF-Net method proposed in this paper is increased by +8.51%, +7.03%, +8.40%, +3.69%, and 6.66%, respectively. Among them, the IFF-Net method achieves significant performance on categories C1, C2, C4, C5, and C6, while C3 is slightly inferior to the Two-branch CNN. The OA of C7 is not superior to other methods due to the small training sample size and the influence of noise in feature fusion, and the overall results also maintain an important quantitative gain compared with other methods. The IFF-Net method proposed in this paper outperforms other classification methods in multimodal remote sensing data classification and achieves better classification accuracy.