## 3 Methods and Data
### Data Augmentation
#### 3.1.2 Data Augmentation  
Currently, data augmentation techniques can be categorized into two main groups: traditional data augmentation methods and image generation algorithms based on generative adversarial networks (GANs), which are relatively new and capable of generating images with similar features to the original dataset but different from them. This approach significantly enhances dataset diversity, thereby improving the model's generalizability [19]. Figure 3: Partial data samples of clouds, ocean, land, and oceanic internal waves. Figure 2: Flow chart of internal waves detection framework. In order to compare the effects of these two data enhancement methods on training models, we applied these two methods to the original data and conducted experimental comparisons. First, we applied ten traditional data augmentation methods to each class of training samples. These methods include color truncation, min-max normalization, standard normalization, flipping, sharpening, Gaussian filtering, random erasing, random brightness transformation, random contrast transformation, and uniform noise. For each augmentation, we randomly selected 130 images, resulting in a total of 1300 augmented samples for each class of training data. When combined with the original dataset, this created a total of 2000 training samples for each class, constituting our Training Set 1. Subsequently, we applied the GAN to data augmentation. Generative adversarial networks (GANs) are a type of deep learning model employed for generating synthetic data, including images [20]. This method leverages two neural networks: a generator and a discriminator. The generator's objective is to produce synthetic data that closely resembles real data by learning the distribution of real data [21]. Conversely, the discriminator is tasked with distinguishing between real and synthetic data. These two networks engage in a competitive process during training, leading to the continual improvement of the generator's ability to produce realistic synthetic data and the discriminator's ability to effectively differentiate between real and synthetic data. The objective function \(V(D;G)\) for the GAN is as follows:  
\[min_{G}max_{D}V(D,G)=E_{X\sim P_{data}(x)}[logD(x)]E_{z\sim P_{z}(z)}[log(1-D(G (z)))] \tag{1}\]  
In the equations provided, where x represents a real sample, \(D(x)\) signifies the probability assigned by the discriminator networks for classifying \(x\) as a real sample. \(G(z)\) corresponds to a sample generated from noise \(z\) by the generator network \(G\), and \(D(G(z))\) indicates the probability assigned by the discriminator network \(D\) for classifying \(G(z)\) as a real sample. Deep convolutional generative adversarial networks (DCGAN) [22] represent a variant of GANs designed to transform noise into images. They excel at generating images that fall within the same category as those present in the training set. DCGANs combine convolutional neural networks (CNNs) with unsupervised learning in the context of supervised learning, finding widespread applications in image generation. In order to facilitate the generation of remote sensing images with dimensions of \(64\times 64\) pixels, we have made modifications to the DCGAN network architecture, as depicted in Figure 4. These modifications include the use of convolutional neural networks as both the generator and discriminator, the implementation of batch normalization for expedited training, the utilization of the LeakyReLU activation function to overcome the limitations of ReLU, the elimination of fully connected layers to prevent overfitting, the adoption of the Adam optimizer, and the capacity to generate high-quality images. We applied the modified DCGAN data augmentation to clouds, land, ocean, and internal waves, with parameters listed in Table 1.  
Figure 4: Modified DCGAN network structure. The modified DCGAN network is trained using the original dataset, with the addition of a dropout layer at the end of each layer in the generator.