## 5 Conclusions  
This paper uses MODIS remote sensing data to produce different types of remote sensing image samples, including internal waves, processes these images, and generates a database including internal waves. In addition, this paper proposes an end-to-end method that uses deep learning technology to improve the recognition performance of MODIS remote sensing images. One of the key contributions is the use of a modified DCGAN network for data augmentation, which significantly improves the diversity of the dataset and enhances the generalization ability of the recognition model. This method has the following advantages: (1) Reduce data collection costs: collecting and labeling large-scale remote sensing datasets is expensive and time-consuming. By using the modified DCGAN to generate data, the time and resources for collecting real data can be effectively reduced; (2) Increase data diversity: The data generated by the modified DCGAN are very similar to real remote sensing images. The data it generates contain a wider range of variations and scenarios, helping network models learn and process more complex real-world data more effectively; (3) Improve generalization ability: The combination of real data and synthetic data improves the generalization ability of the model. This means that the trained model is more able to accurately classify and identify new, unknown data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Overall Accuracy/\%** & **Accuracy of Oceanic Internal Waves Recognition/\%** \\ \hline AlexNet [28] & 95.00 & 95.25 \\ VGG11 [29] & 97.875 & 96.75 \\ VGG16 [29] & 97.50 & 96.25 \\ GoogLeNet [30] & 98.50 & 98.75 \\ Resnet18 [24] & 98.125 & 98.75 \\ WaveNet (ours) & 98.625 & 99.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison on the dataset. Figure 10: Display of test results: (**a**) a whole remote sensing image; (**b**) remote sensing images after being detected. Another contribution lies in the design of the WaveNet network with excellent recognition performance. By adding a channel attention mechanism to the deep convolutional layer, WaveNet can pay different attention to each channel of the feature map, thereby more effectively learning features related to remote sensing classification. Moreover, the convolutional layers of WaveNet have a residual structure, which allows WaveNet to avoid overfitting problems caused by too deep layers in actual training. By combining the above methods, we have improved the recognition accuracy of remote sensing image classification tasks. This has practical applications in many fields, such as environmental monitoring, early warning of marine disasters, detection of internal ocean waves, etc. However, due to the fact that the data come from optical remote sensing satellites, it may be difficult to obtain sea surface data when the weather is bad, resulting in certain limitations in actual citation. In future research, we plan to further explore how to integrate geographical location information into remote sensing images to further improve the practicality of the research. Specifically, we plan to collect data on the geographical coordinates of where remote sensing images were taken and associate these data with the images. Through this association we can achieve the following goals. Geographical information feature extraction: We plan to use geolocation information to extract information related to geographical features in images. For example, we can determine the distance of waves within the ocean from the shoreline in an image, as well as the shape and wavelength of the waves. This information is of great significance to fields such as ocean research. Environmental monitoring and management: Geolocation information can also be used for environmental monitoring and management. We plan to use this information to track changes in specific areas, such as changes in land use or changes in ocean water quality. This will lead to a better understanding and management of natural resources.