# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 3 Results
### Results and Analysis on Augsburg Data
In order to evaluate the stability and robustness of the proposed IFF-Net method, 10 experiments are performed on the test sample data, and the standard deviations of each of the categories are calculated for statistical evaluation. (2) Visual comparison: The classification graph for the Augsburg dataset is shown in Figure 5. GT-Data is the real ground classification map. the SVM classifier considers less spatial information, the classification map is noisy, and the classification effect is not good; especially for the industrial area (C3) and commercial area (C6), a large number of misclassifications pollute the map. The CapsNet and S2 FL classifiers have noise in the classification of the commercial area (C6), which affects the classification effect. Compared with the SVM classifier, the CapsNet and S2 FL classifiers have better classification effects \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class No**} & **SVM** & **SVM** & **SVM** & **LeMA** & **CapsNet** & **Two-Branch** & **S2 FL** & **IFF-Net** & **IFF-Net** \\  & **(HS1)** & **(SAR)** & **(HIS + SAR)** & **(HIS + SAR)** & **(HIS + SAR)** & **(HIS + SAR)** & **(HIS + SAR)** & **(HIS + SAR)** & **(SAR)** & **(HIS + SAR)** \\ \hline C1 & 82.01 \(\pm\) 0.04 & 83.23 \(\pm\) 0.35 & 90.33 \(\pm\) 0.126 & 86.86 \(\pm\) 0.205 & 85.10 \(\pm\) 0.85 & 93.76 \(\pm\) 0.61 & 88.80 \(\pm\) 0.11 & 84.25 \(\pm\) 0.08 & 98.21 \(\pm\) 0.31 \\ \hline C2 & 86.24 \(\pm\) 0.14 & 85.51 \(\pm\) 0.146 & 90.47 \(\pm\) 0.31 & 90.08 \(\pm\) 0.185 & 89.02 \(\pm\) 0.125 & 94.19 \(\pm\) 0.44 & 89.36 \(\pm\) 0.125 & 86.36 \(\pm\) 0.137 & 97.23 \(\pm\) 0.046 \\ \hline C3 & 21.97 \(\pm\) 0.84 & 5.20 \(\pm\) 0.76 & 20.37 \(\pm\) 0.12 & 42.00 \(\pm\) 0.26 & 40.44 \(\pm\) 0.16 & 86.73 \(\pm\) 0.121 & 45.90 \(\pm\) 0.061 & 17.64 \(\pm\) 0.081 & 50.95 \(\pm\) 0.38 \\ \hline C4 & 80.81 \(\pm\) 0.14 & 67.98 \(\pm\) 0.104 & 84.57 \(\pm\) 0.64 & 86.79 \(\pm\) 0.156 & 85.35 \(\pm\) 0.04 & 85.51 \(\pm\) 0.067 & 87.53 \(\pm\) 0.53 & 91.31 \(\pm\) 0.045 & 91.63 \(\pm\) 0.121 \\ \hline C5 & 36.63 \(\pm\) 0.208 & 5.42 \(\pm\) 0.37 & 36.71 \(\pm\) 0.55 & 47.34 \(\pm\) 0.104 & 45.00 \(\pm\) 0.31 & 51.86 \(\pm\) 0.72 & 68.64 \(\pm\) 0.061 & 30.15 \(\pm\) 0.123 & 83.36 \(\pm\) 0.035 \\ \hline C6 & 11.72 \(\pm\) 0.86 & 1.14 \(\pm\) 0.108 & 9.58 \(\pm\) 0.29 & 23.30 \(\pm\) 0.87 & 21.80 \(\pm\) 0.21 & 28.35 \(\pm\) 0.53 & 10.97 \(\pm\) 0.136 & 12.64 \(\pm\) 0.14 & 38.35 \(\pm\) 0.072 \\ \hline C7 & 45.12 \(\pm\) 0.121 & 12.60 \(\pm\) 0.135 & 45.65 \(\pm\) 0.072 & 46.99 \(\pm\) 0.16 & 45.38 \(\pm\) 0.05 & 49.37 \(\pm\) 0.105 & 47.65 \(\pm\) 0.044 & 14.85 \(\pm\) 0.36 & 28.51 \(\pm\) 0.44 \\ \hline OA (\%) & 81.20 \(\pm\) 0.076 & 79.63 \(\pm\) 0.54 & 82.01 \(\pm\) 0.14 & 83.49 \(\pm\) 0.134 & 82.12 \(\pm\) 0.52 & 86.83 \(\pm\) 0.071 & 83.86 \(\pm\) 0.029 & 80.98 \(\pm\) 0.052 & 90.52 \(\pm\) 0.38 \\ \hline AA (\%) & 53.50 \(\pm\) 0.132 & 40.73 \(\pm\) 0.13 & 53.95 \(\pm\) 0.161 & 60.48 \(\pm\) 0.151 & 58.87 \(\pm\) 0.64 & 65.97 \(\pm\) 0.06 & 62.40 \(\pm\) 0.037 & 48.17 \(\pm\) 0.029 & 69.74 \(\pm\) 0.027 \\ \hline \hline \(\kappa\) & 74.53 \(\pm\) 0.087 & 72.41 \(\pm\) 0.46 & 73.74 \(\pm\) 0.62 & 77.54 \(\pm\) 0.32 & 75.80 \(\pm\) 0.38 & 81.91 \(\pm\) 0.59 & 78.03 \(\pm\) 0.049 & 74.19 \(\pm\) 0.063 & 86.74 \(\pm\) 0.067 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Quantitative comparison of different methods in terms of OA, AA, and \(\kappa\) on the Augsburg dataset. for the industrial area (C3) and commercial area (C6). In addition, the method based on deep learning performs well on the overall classification effect, and the classification map is clearer. The Two-branch CNN classifier is better than other classifiers and the classification map is smoother and less noisy, particularly with regards to the delineation of boundaries between vegetation (C1 and C4) and C7 bodies.