# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 3 MCNN Framework for Geophysical Phenomenon Classification
### Multi-Feature Fusion  
For each original input SAR image, the MCNN algorithm requires resizing the corresponding dimensions to 299 pixels for both height and width. Nevertheless, in the direct implementation of oceanic phenomenon classification, there is a potential for the loss of certain meaningful features and the attenuation of information that is considered less pertinent [57]. Hence, the an image edge filter is implemented to retain the critical structural attributes inherent in SAR magettes. Table 1 lists the gradient magnitudes of different image edge filters with specified operators. Then the resultant and the initial are concatenated to form the input image channels. Experiments are conducted on the TenGeoP-SARwv dataset to determine the optimal channel combination for oceanic phenomenon classification. The empirical findings indicate that the optimal performance can be achieved when utilizing a multichannel input image reconstructed from both the original image and the central difference gradient. When employing the channel set combined only with the gradient magnitude, the classification accuracy sharply decreases. This can be attributed to the redundancy contained in the gradient magnitude. This does not contribute significantly to the enhancement of classification accuracy. Instead, information pertaining to interference is utilized as an alternative approach. Hence, the input image in this study is resized to the dimensions of \(299\times 299\times 3\), incorporating three channels with two original SAR images and a central difference gradient. The combination of multiple SAR images facilitates the creation of an intricate oceanic scene representation, consequently enhancing the precision of decision-making in subsequent tasks. Nonetheless, the efficacy of image fusion critically depends on the stringent geometric alignment of magettes. Hence, we design and implement the feature-based image registration method to establish an accurate correlation between magettes and assess the spatial transformation accordingly. In our design, we assume that the spatial resolution of both images is known and remains constant in our scenario. Because the pixel-by-pixel computations and variable resolution correlation can result in a time-intensive implementation, we initially conduct a normalized cross-correlation between the images at a reduced resolution, aiming to approximately delineate the common area of overlap between the two images. High-resolution correlation is subsequently assessed in close proximity to each potential registration point. The variables \(IM_{1}\) and \(IM_{2}\) denote two SAR images with both images having dimensions \(M\times N\) and \(f\) represents the image zoom factor. Let \(IM_{1}\) and \(IM_{12}\) denote the low-resolution versions of the two SAR images with dimensions \(Ml\times Nl\). The edged images of the corresponding images are represented by \(IM_{1e},IM_{2e},IM_{1e},IM_{12e}\), respectively. In this context, the generalized cross-correlation can be mathematically expressed as follows:  
\[\phi_{1}(u,v)=\frac{\sum_{i=0}^{Ml}\sum_{j=0}^{Nl}(IM_{1 Leu,v}(i,j)-IM_{12e}(i, j))}{\sigma_{1u,v}\sigma_{2l}}, \tag{8}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Edge Filter** & **Description (\(x\) and \(y\) Denote the Horizontal and Vertical Directions, Respectively)** \\ \hline
1 & Central & Weighted difference of neighboring pixels: \(\frac{dl}{dy}=\frac{l(y+1)-l(y-1)}{2}\) and \(\frac{dl}{dx}=\frac{l(x+1)-l(x-1)}{2}\) \\
2 & [PERSON] & Convolution mask: \(\Delta y=\begin{bmatrix}1&1&1\\ 0&0&0\\ -1&-1&-1\end{bmatrix}\)and \(\Delta x=\begin{bmatrix}-1&0&1\\ -1&0&1\\ -1&0&1\end{bmatrix}\) \\
3 & [PERSON] & Weighted difference between diagonally adjacent pixels: \(\Delta_{1}=\begin{bmatrix}0&1\\ -1&0\end{bmatrix}\) \\
4 & [PERSON] & Convolution mask: \(\Delta y=\begin{bmatrix}1&2&1\\ 0&0&0\\ -1&-2&-1\end{bmatrix}\)and \(\Delta x=\begin{bmatrix}-1&0&1\\ -2&0&2\\ -1&0&1\end{bmatrix}\) \\ \hline \hline \end{tabular}
\end{table}