# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 3 Results
### Experimental Setup
The running times of the classification methods employed in this study on the experimental dataset are presented in Table 3.  
The proposed IFF-Net network in the paper primarily accomplishes feature extraction, propagation, and calibration for multimodal remote sensing images without the need for manual tuning or parameter setting. To ensure a fair comparison with other network architectures, the designed IFF-Net structure in this study closely resembles existing network structures while maintaining similar parameters and complexity. Hyperparametric analysis of the image block size \(r\): A specific comparison of the image block sizes \(r\) and OA of the two datasets is performed. The \(r\times r\) square neighborhood represents the spatial information of the central pixel, \(r=[5,7,9,12,13,15]\). As can be seen from Table 4, \(r\) is positively correlated with OA in most cases. When the image block size \(r\) is large enough, the improvement in OA is insignificant. As the spatial information is improved by multimodal information fusion, the performance of a small block of images is more satisfactory for the IFF-Net method. Through comprehensive comparison and analysis, it can be seen through the above Table 4 that a more satisfactory performance can be obtained by using \(r\) = 12 for the Berlin dataset and Augsburg dataset. Therefore, a larger patch size can reduce the interference of mixed boundaries, but for urban land cover classification the feature boundaries are clear and continuously distributed. Larger image patch sizes are prone to artefacts and increase computational burden \begin{table}
\begin{tabular}{c c c} \hline \hline \(r\) & **Berlin (OA\%)** & **Augsburg (OA\%)** \\ \hline
5 & 68.21 & 88.36 \\ \hline
7 & 68.86 & 88.58 \\ \hline
9 & 69.85 & 88.96 \\ \hline
12 & 71.02 & 90.52 \\ \hline
13 & 69.92 & 86.42 \\ \hline
15 & 68.91 & 86.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of OA and image block size. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Method** & **Time(s) on Berlin** & **Time(s) on Augsburg** \\ \hline SVM & 276.21 & 256.51 \\ \hline CapsNet & 280.26 & 262.36 \\ \hline S2 FL & 157.18 & 121.68 \\ \hline LeMA & 164.12 & 152.32 \\ \hline Two-branch CNN & 371.42 & 312.12 \\ \hline IFF-Net & 421.65 & 361.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Running times on the experimental datasets. Hyperparametric analysis of the number of residual blocks \(n\): Based on experience, it is known that more convolutional layers need to fit more parameters and are beneficial to image classification, where overfitting can easily occur in the case of insufficient training samples. From Table 5, it can be seen that when the number of residual blocks \(n>2\), the OA value gradually decreases. When \(n=1\), the IFF-Net method displays underfitting, which affects the classification performance. Therefore, the IFF-Net method proposed here chooses \(n=2\) as the hyperparameter of residual blocks. The loss function of the proposed method is defined as presented in Equation (1), and its utilization aids in regularization optimization. In this study, the L1 norm is employed to impose sparsity constraints on certain judgment factors. \(\lambda\) represents a regularization factor that effectively balances the weights between the two loss measures. Table 6 illustrates the correlation between different values of \(\lambda\) and OA. Notably, when \(\lambda=0.05\), the IFF-Net model introduced here demonstrates superior performance on both datasets. Conversely, for larger values of \(\lambda>0.1\), an increased value leads to a degradation in focus within the classification component. Consequently, based on our findings, we select \(\lambda=0.05\) as the optimal regularization factor for IFF-Net.