# A Deep Curriculum Learning Semi-Supervised Framework for Remote Sensing Scene Classification
## 3 Proposed Method
### Multimodal Curriculum Learning
We visualize the curriculum images selected by DCLSSF, as shown in Figure 8. We can see that, at the beginning of CL, the easier samples are selected for label propagation, and in the later stages, the more difficult samples are selected for learning. For example, in the agriculture category, the initial image is easily recognizable from the texture, and the final sample changes color and texture. ### Training and Testing Times

Computational times are reported for the setting with 80% and 20% training and testing images for the UC Merced dataset. The configuration of the computer is mentioned in Section 4.1. Table 6 reports the training and testing times in the UC Merced dataset. We reported testing time on a per-image basis, given that users of the algorithm may have the need to classify a sub-group or even a single image instead of the entire testing set of testing images. All experimental training time includes feature fusion time and multimodal curriculum learning training time. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & & **DCA** & **DCCA** \\ \hline UC Merced & Training & 280.34 & 27.748 \\  & Testing & \(3.23\times 10^{-3}\) & \(1.27\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The total training time and the testing time per image (seconds) in UC Merced dataset. Figure 8: In the multimodal curriculum learning, the curriculum selection is from easy to difficult. ## 5 Conclusions

This paper proposes a DCLSSF method which can achieve step-by-step learning through multimodal curriculum learning. Deep features have strong feature representation capabilities, and the deep features of different deep learning networks are regarded as different modalities. Then, through deep feature fusion, the attainment of high-level semantic features has both strong discriminatory power and low dimensionality. Finally, the semi-supervised classification is carried out by the multimodal curriculum learning method. By optimizing the learning sequence, it is possible to perform semi-supervised classification better and obtain a better recognition rate. Experiments show that the DCLSSF model can surpass many supervised classification methods. Considering that fine-tuning a deep learning network usually takes several hours to a day of training time, the proposed algorithm strikes a good balance between accuracy and efficiency. In the multimodal curriculum learning method, an image is regarded as a node of the graph, and as the size of the dataset increases, the size of the graph will also increase, and its operation will increase geometrically, so this method is only suitable for datasets with a small data scale, i.e., the number of images in the dataset cannot exceed 100,000. In this work, we have only considered the reliability of the curriculum. In the future, the diversity of the curriculum could be taken into account for curriculum selection. Conceptualization, Q.Z. and B.Y. ; methodology, Q.Z. ; software, J.C.; validation, Q.Z. and J.C.; writing--original draft preparation, Q.Z. ; writing--review and editing, B.Y. ; supervision, B.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the Jiangsu Engineering Research Center of Digital Twinning Technology for Key Equipment in Petro- chemical Process under Grants DTEC 202304. Not applicable. The data present in this study are publicly available in the NWPU-RESISC45 dataset at [[https://hf-mirror.com/datasets/timm/resisc45](https://hf-mirror.com/datasets/timm/resisc45)]([https://hf-mirror.com/datasets/timm/resisc45](https://hf-mirror.com/datasets/timm/resisc45)) (accessed on 1 December 2024). The code is available at [[https://github.com/bhcczu/DCLSSF](https://github.com/bhcczu/DCLSSF)]([https://github.com/bhcczu/DCLSSF](https://github.com/bhcczu/DCLSSF)), [[https://github.com/bhcczu/DCLF](https://github.com/bhcczu/DCLF)]([https://github.com/bhcczu/DCLF](https://github.com/bhcczu/DCLF)).