# A Deep Curriculum Learning Semi-Supervised Framework for Remote Sensing Scene Classification
## 3 Proposed Method
### Multimodal Curriculum Learning
Accordingly, the classes that scored best (100%) in the F1-score metric are agricultural, airplane, beach, harbor and parking lot using DCA fusion method, as shown in Table 2. To distinguish \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Method Name** & **10\% Training Set** & **50\% Training Set** & **80\% Training Set** \\ \hline \multirow{2}{*}{Traditional methods} & Sparse coding+SVM & 45.64 \(\pm\) 1.02 & 52.61 \(\pm\) 1.59 & 58.56 \(\pm\) 1.29 \\  & Dictionary learning+SVM & 46.89 \(\pm\) 1.57 & 54.37 \(\pm\) 1.34 & 59.11 \(\pm\) 0.98 \\ \hline \multirow{4}{*}{Supervised learning} & GoogleNet [31] & 83.64 \(\pm\) 2.29 & - & - \\  & VGG-F [32] & 85.61 \(\pm\) 1.06 & - & - \\  & ResNet [28] & 88.90 \(\pm\) 2.48 & - & - \\  & ARCNet-VGG16 [33] & - & 96.81 \(\pm\) 0.14 & 99.12 \(\pm\) 0.40 \\  & GBN [34] & - & 97.05 \(\pm\) 0.19 & 98.57 \(\pm\) 0.48 \\ \hline \multirow{2}{*}{Unsupervised learning} & Attention-GAN [35] & - & 89.06 \(\pm\) 0.50 & 97.69 \(\pm\) 0.69 \\  & Self-training(ResNet) [12] & - & 91.57 \(\pm\) 2.00 & - \\ \hline \multirow{4}{*}{Semi-Supervised learning} & Co-training [11] & 93.75 \(\pm\) 1.42 & - & - \\  & SSGA-E [12] & 94.52 \(\pm\) 1.38 & - & - \\  & Fixmatch [10] & 96.22 \(\pm\) 0.21 & - & - \\  & Mixmatch [9] & 95.45 \(\pm\) 0.43 & - & - \\  & SS-RCSN [8] & 98.10 \(\pm\) 0.23 & - & - \\  & Our Method with DCA & 93.19 \(\pm\) 0.32 & 96.81 \(\pm\) 0.21 & 99.10 \(\pm\) 0.27 \\  & Our Method with DCCA & 99.14 \(\pm\) 0.18 & 99.53 \(\pm\) 0.22 & 99.81 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy (%) of different methods on UCM dataset. Figure 3: Examples of the UC Merced dataset. the intra-class diversity of the images is increased. This characteristic can make it difficult to generalize the model, requiring methods that are robust to variability within categories. An example of the AID dataset is shown in Figure 5.

In order to evaluate the performance of the proposed method on the AID dataset, follow the same experimental settings used in AID [8]. For example, one is 10-90%: the images in each class in the dataset are randomly divided into 10% for training and 90% for testing. Figure 5: Examples of the AID dataset. Table 3 shows the accuracy comparisons on the AID dataset. It can be seen that the proposed method achieves higher results than traditional methods, supervised learning methods, unsupervised learning methods, and SSL approaches. The confusion matrix with 20% training ratios is shown in Figure 6. The recognition rate is relatively low in categories such as \"center\", \"resort\", \"school\", and \"square\". The most confusing between resort and park categories is probably because some parks are built for leisure. These parks and resorts may contain green belts, lakes, etc., so it is easy to confuse. In addition, the \"center\" and \"square\" categories are also easy to confuse, which may be because the two have a similar appearance from the sky. For the classification of these confusing categories, the network is required to extract more discriminative and robust features, so as to achieve an accurate identification of intra-class variability. ### Comparisons on NWPU RESICS-45 Dataset

The NWPU RESICS-45 dataset has a total of 31,500 remote sensing images, each category has 700 images, and each image is 224 \(\times\) 224 in size. The dataset has 45 categories: airplane, baseball diamond, basketball court, beach, bridge, chaparral, church, mountain,