# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 5 Conclusions  
To address the challenges associated with low efficiency in global ocean SAR scene classification, a novel robust classification model, denoted MCNN, that included multi-feature fusion, data augmentation, and multichannel feature extraction was successfully developed based on the Inception-ResNet CNN architecture. In a departure from conventional deep learning methodologies, a meticulously selected image edge filter was employed to distinguish distinct oceanic phenomena. Furthermore, several data augmentation and feature extraction techniques were implemented to mitigate the limitations posed by small-sample training datasets. The model performance was rigorously validated on a meticulously annotated dataset [70], focusing on the detection of ten geophysical phenomena prevalent on the ocean surface. Comparative analyses were conducted against classical and state-of-the-art methods. Additionally, our MCNN algorithm was applied to generate a geophysical map of RCs, demonstrating acceptable consistency with precipitation data obtained from the GPM Program. These results underscore the potential utility of the MCNN tool in obtaining global/regional and annual/seasonal statistics of diverse geophysical phenomena and enhancing specific aspects of numerical ocean models. While this study exclusively utilized S-1 WV SAR imagettes, the adaptability of the MCNN algorithm to other SAR data archives, such as GF-3 and TerraSAR-X, is acknowledged. Despite the notable contributions of this study, it is essential to acknowledge certain limitations. The analysis is constrained to SAR images featuring ten common types of ocean phenomena, and there may be occurrences beyond the scope of these datasets that could  
Figure 8: Annual comparison of MCNN-detected RCs (**b**) with CMwv-detected RCs (**a**) and GPM precipitation measurements (**c**). The rain occurrence percentages are calculated within each 5\({}^{\circ}\) by 5\({}^{\circ}\) spatial bin based on the TenGeop-SARwv database from March 2016 to February 2017. The associated average yearly precipitation is obtained from the GPM late-run product. influence the generalizability of the findings. Moreover, the effectiveness of the proposed methods may vary in the context of diverse datasets or under conditions not represented in the current study. As a result, the robustness and accuracy of the model may be limited when recognizing ocean phenomena beyond the scope of our dataset. These limitations emphasize the importance of future research to explore a more comprehensive range of datasets and scenarios for a thorough understanding of the model's capabilities. In future studies, our objective is to augment the dataset by including SAR images of additional ocean phenomena, thereby bolstering the model's robustness. The expansion of the dataset will enable us to tackle challenges associated with imbalanced categories and enhance the model's adaptability to new samples. This approach seeks to improve the model's generalization capabilities and contribute to more effective and comprehensive recognition of diverse ocean phenomena. This methodology holds potential for broader applications, such as weather prediction and hazard forecasting through improved classification rates. The enhanced performance of accurate recognition and classification for ocean phenomena in SAR images may contribute to more reliable scarce data inputs for weather prediction models, ultimately leading to increased precision in forecasting and hazard prediction. The authors confirm contribution to the paper as follows: conceptualization, C.B. and S.Z. ; methodology, C.B. and S.Z. ; software, S.Z. and J.W. ; validation, S.Z., C.B. and X.W. ; formal analysis, C.L. ; investigation, J.W. ; resources, C.B. and S.Z. ; data curation, C.L. and J.W. ; writing--original draft preparation, C.B. and S.Z. ; writing--review and editing, X.W., J.W. and C.L. ; visualization, J.W. ; supervision, C.L. ; project administration, X.W. ; funding acquisition, C.B.