# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 4 Experimental Evaluation
### Geophysical Application  
To further validate the robustness of our MCNN algorithm, this classification model was systematically applied to all SAR imagettes spanning the period from March 2016 to February 2017 within the TenGeop-SARwv dataset. Subsequently, a geophysical map \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{6}{c}{**Classification Results of the Different Methods**} \\ \cline{2-10}  & \multicolumn{2}{c}{**AlexNet**} & \multicolumn{2}{c}{**CMwv**} & \multicolumn{2}{c}{**MCNN**} \\  & **Recall** & **Precision** & **F-Score** & **Recall** & **Precision** & **F-Score** & **Recall** & **Precision** & **F-Score** \\ \hline Atmospheric fronts & 0.46 & 0.72 & 0.56 & **0.95** & 0.40 & 0.56 & 0.93 & **0.83** & **0.88** \\ (AFs) & 0.13 & 0.81 & 0.22 & **0.95** & 0.38 & 0.54 & 0.92 & **0.91** & **0.91** \\ Biological slicks & 0.97 & 0.67 & 0.79 & **0.95** & 0.88 & 0.91 & 0.89 & **0.99** & **0.94** \\ (BSs) & 0.63 & 0.7 & 0.66 & 0.89 & 0.91 & 0.9 & **0.93** & **1** & **0.96** \\ Icebergs & 0.48 & 0.66 & 0.56 & **0.97** & 0.16 & 0.27 & 0.96 & **0.98** & **0.97** \\ (IBs) & 0.04 & 0.82 & 0.08 & 0.92 & 0.18 & 0.3 & **0.97** & **0.97** & **0.97** \\ Low-wind areas & 0.76 & **0.98** & 0.86 & **1** & 0.87 & **0.93** & 0.92 & 0.94 & **0.93** \\ (LWAs) & 0.74 & 0.78 & 0.76 & **1** & 0.79 & 0.88 & 0.96 & **0.95** & **0.95** \\ Microconvective cells & 0.38 & 0.88 & 0.53 & 0.8 & 0.76 & 0.78 & **0.98** & **0.98** & **0.98** \\ (MCCs) & 0.41 & 0.82 & 0.55 & 0.85 & **0.94** & 0.89 & **0.95** & 0.93 & **0.94** \\ Oceanic fronts & 0.41 & 0.88 & 0.56 & **1** & 0.05 & 0.1 & 0.91 & **0.92** & **0.91** \\ (OFs) & 0.67 & 0.48 & 0.56 & **1** & 0.05 & 0.1 & 0.95 & **0.87** & **0.91** \\ Pure ocean waves & 0.91 & 0.81 & 0.86 & 0.47 & **1** & 0.64 & **0.98** & 0.95 & **0.96** \\ (PWs) & 0.12 & **0.95** & 0.21 & 0.39 & 0.98 & 0.56 & **0.96** & **0.95** & **0.95** \\ Rain cells & 0.66 & 0.88 & 0.75 & 0.93 & 0.88 & 0.9 & **0.97** & **0.99** & **0.98** \\ (RCs) & 0.73 & 0.88 & 0.8 & 0.93 & 0.8 & 0.86 & **0.98** & **0.96** & **0.97** \\ Sea ice & 0.75 & 0.93 & 0.83 & 0.9 & 0.96 & 0.93 & **0.96** & **1** & **0.98** \\ (SI) & 0.85 & 0.74 & 0.79 & 0.96 & 0.96 & 0.96 & **0.99** & **0.98** & **0.98** \\ Windstreaks & 0.53 & 0.72 & 0.61 & 0.83 & 0.77 & 0.8 & **0.99** & **0.98** & **0.98** \\ (WSs) & 0.79 & 0.58 & 0.67 & **0.83** & 0.96 & 0.89 & **0.83** & **1** & **0.91** \\ \hline Average & 0.63 & 0.81 & 0.71 & 0.88 & 0.67 & 0.76 & **0.95** & **0.96** & **0.95** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification performance in WV1 (denoted by expansion) and WV2 (denoted by abbreviation) imagette detection of the different methods (the highest values are indicated in bold). delineating detected RCs was generated and meticulously compared with precipitation data sourced from the GPM Program. Within the context of S-1 magettes, a classified RC was defined as comprising several kilometer-scale semicircular to circular-shaped patches, characteristic of rain downdrafts in convective RCs, as elaborated in [50]. Over the specified timeframe, encompassing March 2016 to February 2017, it was observed that approximately 5% of all SAR magettes were successfully classified as RCs by the MCNN algorithm. The cartographic outcome of the classified RC occurrences, as depicted in the central panel of Figure 8, indicates discernible spatial patterns. The geophysical map presenting the yearly averaged GPM precipitation, along with the classified outcomes derived from the CMwv algorithm, is concurrently displayed in the right and left panels, respectively. It is imperative to acknowledge that the GPM product is designed to furnish gridded global multisatellite precipitation estimates, resulting in notable disparities in coverage resolutions between the SAR-classified RC occurrences and the GPM precipitation results.