# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 4 Experimental Evaluation
### Experimental Results of Ocean SAR Scene Classification  
The TenGeoP-SARwv dataset has been employed for training our MCNN algorithm through a series of iterations exceeding 2000. As depicted in Figure 6, the comprehensive accuracy and loss metrics exhibit a notable and rapid evolution within the initial 270 iterations, stabilizing thereafter around the 1000 th iteration. Consequently, the neural network weights derived from the training process up to 1100 iterations are incorporated into the ultimate configuration of the MCNN model.  
Figure 7 illustrates the normalized confusion matrix obtained from the MCNN model. The diagonal and off-diagonal elements of the matrix correspond to accurately and inaccurately classified observations, respectively, normalized with respect to the total number of observations. The percentages indicated in the blue cells within the row (column) summaries on the right (bottom) pertain to recall (precision) metrics. Additionally, F-score parameters, as defined by [PERSON] et al. [67], are employed as evaluation metrics, offering a  
comprehensive assessment considering both precision and recall statistics. The anticipated values for these three parameters are all in proximity to unity.  
Figure 6: Variation of accuracy (%) and loss values with different iterations of the MCNN algorithm for WV1 (**a**,**b**) and WV2 (**c**,**d**) imagette classification. In the upper part, the accuracy curve illustrates the network’s training process, demonstrating a consistent increase in accuracy until reaching nearly 90%. In the lower part, the loss curve depicts the network’s training progress, consistently decreasing until approaching nearly 0.  
Figure 7: Normalized confusion matrix of the MCNN algorithm when applied to WVI (**a**) and WV2 (**b**). From the confusion matrix, it is evident that the true positive rates for most classes are above 90%, except for the class BS with WV1 applied. Additionally, with WV2 applied, the true positive rates for most classes are again above 90%, except for the class WS.  
Table 2 provides a comparative analysis of distinct classification models based on metrics such as recall, precision, and F-score. The interpretation of the table is as follows:  
1. Despite the established robustness of AlexNet in image classification, its performance in ocean SAR scene classification falls short of acceptability without the incorporation of sophisticated domain knowledge.
2. The experimental results evaluate the effectiveness of the CMwv model in accurately identifying biological slicks (BS), low-wind areas (LWA), microconvective cells (MCC), rain cells (RC), sea ice (SI), and windstreaks (WS). However, suboptimal results are evident when employing the CMwv model to detect other geophysical phenomena, with a notable instance being the lowest precision value recorded at 0.05 for the oceanic front (OF) class.
3. The MCNN algorithm demonstrates superior performance in terms of recall, precision, and F-score, all surpassing 0.9 for both WV1 and WV2 imagettes, with the exception of the LWA class. Despite the limited number of samples available for training, the MCNN algorithm leverages data augmentation and transfer-learning strategies effectively, mitigating the risk of model overfitting. Furthermore, the utilization of an image edge filter optimally encodes discriminant information present in SAR imagettes. On average, the MCNN algorithm yields recall, precision, and F-score values that are 7.9% (8.0%), 43.2% (35.7%), and 25.0% (21.7%) higher, respectively, compared to CMwv when applied to WV1 (WV2) imagette classification. These results signify the potential of the proposed CNN-based model as a viable operational tool for automated ocean SAR scene classification.