# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 3 MCNN Framework for Geophysical Phenomenon Classification
### Multichannel Feature Extraction  
Despite previous endeavors to augment the dataset, the imperative remains to devise an innovative feature extraction architecture. While employing SAR images for identifying ocean phenomena, we confronted the challenge of the model lacking robustness. The model proved sensitive to the impact of noise and abnormal samples. Given the constraints of the constructed dataset with limited original training data, the construction of a medium-sized network, such as Inception-ResNet, becomes indispensable to fulfill the demands of multichannel feature extraction. To capture the structural characteristics of ocean SAR imagettes, we implement the Inception-ResNet architecture, which is widely recognized for its intricate multi-branch design in Figure 5. As depicted in Figure 5, the data first pass through a stem network composed of pooling layers and convolutional layers. Then, they undergo dimension reduction in the network's Inception-Resnet and reduction parts, which serve to reduce the spatial dimensions. After feature extraction achieved by the aforementioned structure, the output section maps the one-dimensional vector to the predicted results. The stem configuration assumes a critical function in both feature extraction and the consequential reduction in spatial dimensions in the input. Within this network, a series of convoluti  
Figure 5: Multichannel features extraction. The network involves a stem structure composed of convolutional layers and pooling layers, a reduction part composed of Inception-Resnet and a reduction structure, and an output part. Inception A to C and reduction A and B are described at the bottom of the figure, where Cv and MP stand for convolutional layer and max pooling layer, respectively. are integrated through concatenation, indicating the network's capacity for complicated feature representation. Inception-ResNet architectures denoted as A, B, and C in Figure 5 consist of both pure and residual inception blocks. The pure configuration, characterized by a split-transform-merge architecture, endows a potent representational capability owing to the presence of dense layers. To expedite gradient propagation through multiple layers, we leverage the residual function, renowned for its efficacy in facilitating information flow and accelerating gradient propagation through layers. It achieves this by approximating a mapping function from the input:  
\[y=F(x)+x. \tag{17}\]  
Following the establishment of the MCNN architecture, it becomes imperative to specify the loss function, quantifying the disparity between the model predictions and the ground truth associated with the input data. In order to mitigate potential class imbalance issues inherent in the classification task, a weighted balanced cross-entropy loss function, as proposed by [21], is adopted for training purposes in Equation (18):  
\[L_{x}(y,\hat{y})=-\frac{1}{M}\left(\sum_{i=1}^{M}\left(\sum_{j=1}^{C}\theta_{ j}y_{ij}log\hat{y}_{ij}\right)\right). \tag{18}\]  
Let \(y\) and \(\hat{y}\) represent the true label and the label predicted by the neural network, respectively, on the training dataset \(X\) sampled from the TenGeoP-SARwv database. For the true label, we assign numerical values to categorical classes (i.e., AF \(\to\) 0, BS \(\to\) 1, IB \(\to\) 2, LWA \(\to\) 3, MCC \(\to\) 4, OF \(\to\) 5, PW \(\to\) 6, RC \(\to\) 7, SI \(\to\) 8, WS \(\to\) 9, shown in Figure 2). For the predicted label, the whole MCNN model takes input data and produces output predictions between 0 and 9. Here, \(M\) signifies the total number of samples in \(X\), and \(C\) denotes the number of distinct oceanic phenomena types. Additionally, \(\theta_{j}\) denotes the weight assigned to the samples belonging to the \(j\)-th class. The computation of a loss value for the entire set \(X\) is a computationally intensive task. To address this challenge, a transfer-learning strategy is implemented in this study. Specifically, half of the layers within the Inception-ResNet architecture are preserved in a frozen state.