# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 3 Results
### Result Analysis  
In the original BigEarthNet paper [32], the highest precision score achieved by the authors in training was 80.05% for the ResNet50 model. However, with the MMDL-Net model, we achieved a precision score of 83.52%. In a recent study by [PERSON] et al. [33], benchmark testing was conducted on the BigEarthNet dataset using 62 DL models for multi-label, multi-class LULC single-image classification tasks. These models included ResNet50, ResNet101, ResNet152, WRN-B4-ECA, among others. The WRN-B4-ECA network achieved the best overall accuracy score of 82.4% for them. To better evaluate the performance of the models and the improvement achieved, comparative experiments were conducted on ResNet50, ResNet101, ResNet152, and MMDL-Net. The performance metrics of MMDL-Net and ResNet are shown in Table 4.  
It can be observed that as the network depth increases, the performance gradually improves with ResNet models. However, there is still a significant gap compared to MMDL-Net. This indicates that although deeper network structures can generally capture more complex features and potentially provide better performance to some extent, the performance gain of ResNet tends to plateau as the network depth continues to increase. This suggests that simply increasing the depth of the network is insufficient to adapt to the classification of multi-band, multi-label remote sensing images. This phenomenon may be caused by various factors such as overfitting, vanishing gradients, or saturation of learnable features in the given dataset. However, our MMDL-Net model has been improved in various aspects to better adapt to the classification task of remote sensing images, resulting in better performance than the ResNet model. MMDL-Net and ResNet evaluation results are compared in Figure 7.  
Continue to observe the precision of the 19 categories in the application classification of remote sensing images, and it is found that the performance of the MMDL-Net model has improved significantly in most categories. This indicates that our MMDL-Net model has achieved commendable results in these 19 classifications. The precision metrics of the MMDL-Net and three types of ResNet for the 19 categories are shown in Table 5.  
Overall, it is evident that the MMDL-Net model outperforms the standard ResNet in most categories. The highest recognition precision occurs in the \"Marine water bodies\" category, which may be due to their relatively homogenous features that are easier to identify. The lowest recognition precision is observed in the \"Beaches, dunes, sand areas\" category, possibly because the features of these landforms are more complex and difficult to discern. For landforms such as \"Pastures\" and \"Agroforestry areas\", the performance of the MMDL-Net model is significantly better than that of the standard ResNet, likely due to the superiority of the MMDL-Net model in handling such complex features. The MMDL-Net model performs better when dealing with complex landform features. The average Precision scores for the classification results of MMDL-Net and ResNet are illustrated in Figure 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Accuracy (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline MMDL-Net & 83.52 & 77.08 & 77.30 & 77.97 \\ ResNet50 & 75.56 & 60.86 & 70.30 & 70.53 \\ ResNet101 & 79.97 & 65.46 & 75.19 & 75.15 \\ ResNet152 & 80.51 & 66.01 & 75.52 & 75.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics of MMDL-Net and ResNet. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Positive** & **Negative** \\ \hline True & True Positive (TP) & True Negative (TN) \\ False & False Positive (FP) & False Negative (FN) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Values of different dimensions for calculating evaluation metrics. Figure 8: Classification results of the average precision scores for MMDL-Net and ResNet.