## 3 Methods and Data
### Transfer Learning  
In the domain of deep learning models, training models with numerous hidden layers and parameters often demand a substantial amount of high-quality labeled data, which can result in significant time and computational costs. However, when dealing with satellite imagery of oceanic internal waves, data availability is frequently limited, presenting a challenge in training an effective deep learning model with a small dataset. To address this limitation, transfer learning has emerged as a valuable approach in deep learning model training, alleviating the data requirement.  
Transfer learning involves leveraging a pre-trained model, which is then fine-tuned for a new task. The fundamental concept of transfer learning is to initially train the network model parameters using large-scale datasets and subsequently fine-tune them for the specific image recognition task at hand. This approach greatly assists the classifier in performing image recognition tasks, even when confronted with limited data resources.  
The EuroSAT remote sensing dataset, as described in reference [25], comprises 10 distinct scene categories, including agricultural land, forest, herbaceous vegetation, highways, industrial areas, pastures, permanent crops, residential areas, rivers, and lakes, with a total of 3000 samples for each category. The primary objective of this study is to evaluate the performance of employing the pre-trained WaveNet network for classification tasks using the EuroSAT remote sensing dataset. Detailed parameter settings for this study are provided in Table 4. These settings cover various aspects of the model and training process, offering a comprehensive overview of the experimental configuration and methodology employed in the classification task using the EuroSAT dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Batch Size** & **Learning Rate** & **Training Epochs** & **Optimizer** \\ \hline
32 & 0.001 & 500 & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pre-trained network parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Layer** & **Input Feature Map Size** & **Output Feature Map Size** \\ \hline
3 × 3 Conv & 3 × 64 × 64 & 64 × 64 × 64 \\
2 × 2 Max Pooling & 64 × 64 × 64 & 64 × 32 × 32 \\ Residual Block & 64 × 32 × 32 & 128 × 32 × 32 \\ SE Residual Block & 128 × 32 × 32 & 128 × 32 × 32 \\ Residual Block & 128 × 32 × 32 & 256 × 16 × 16 \\ SE Residual Block & 256 × 16 × 16 & 256 × 16 \\ Residual Block & 256 × 16 & 512 × 8 × 8 \\ SE Residual Block & 512 × 8 × 8 & 512 × 8 8 \\ Global Average Pooling & 512 × 8 × 8 & 512 × 1 \\ Fully Connected & 512 × 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Size changes of feature map.  
During the fine-tuning stage, the parameters of the WaveNet network model are initialized with pre-trained parameters, and the output of the last fully connected layer is adjusted to 4 in order to perform classification on the MODIS image dataset. Utilizing a pre-training strategy instead of initializing the network parameters with random weights allows the WaveNet model to initially learn rich texture features from the EuroSAT dataset. This approach not only eliminates the need to train the model from scratch but also helps overcome potential overfitting issues, thereby enhancing the model's performance in situations with limited data. By leveraging pre-training, the model can benefit from the learned representations, enabling it to generalize more effectively and achieve higher performance even when the available data are limited.