# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 2 Dataset and Data Processing
### Data Processing
\(C(i,j)\) stands for the intensity of SAR image pixels with the coordinate \((i,j)\). The variable \(x(i,j)\) represents the intensity of SAR image pixels without speckle noise, and \(n(i,j)\) represents the intensity of speckle noise. To eliminate speckle noise in SAR images, a Lee filter is designed for preserving edges and point features in speckle noise reduction [54]. In a Lee filter, a variable-sized window is moving consistently from left to right and top to bottom across the SAR image to compute the local statistic. To sustain the characteristics of oceanic phenomena, our Lee filter is based on a linear speckle noise model and minimum mean square error methods to enhance the data quality as follows:  
\[\hat{Y}(t)=\bar{I}(t)+W(t)[I(t)-\bar{I}(t)], \tag{3}\]  
where \(\bar{I}(t)\) stands for the mean value of the intensity within the filter window; \(W(t)\) represents the adaptive filter coefficient defined as Equation (4):  
\[W(t)=1-\frac{C_{v}}{C_{I}}. \tag{4}\]  
The variable \(\hat{Y}(t)\) represents the intensity of the filtered SAR images in Equation (3). \(I(t)\) is the intensity of global pixels without noise. Typically, the value of \(W(t)\) tends to zero in homogeneous regions, yielding analogous outcomes to that achieved by the mean filter. Meanwhile, the value of \(W(t)\) tends towards edges, leading to pixel alteration in close proximity to these edges. The impact of the Lee filter is clearly exhibited in Figure 1.  
To improve the overall visual quality of imagettes, we apply global histogram equalization in image processing by redistributing the intensity values of the pixels in an image [55], aiming to maximize the full dynamic range available. This method involves computing the cumulative distribution function of the image's pixel intensities and then adjusting the intensity values based on this cumulative distribution. In our data processing, we first calculate a probability density function (PDF) of the pixel intensity of an image, as defined in Equation (5):  
\[p(X_{k})=\frac{n_{k}}{N},\quad for\,k=0,1,\dots,L-1. \tag{5}\]  
The variable \(n_{k}\) stands for the number of image pixels with an intensity that equals \(X_{k}\). The variable \(N\) represents the total number of image pixels. To ensure that specific intensity values are assigned to each pixel in our imagettes, we apply a ceiling operation to round the intensity values to the nearest integer. This rounding operation ensures that each pixel is associated with a discrete and unique integer intensity value, contributing to the creation of a PDF based on these rounded values. For instance, the rounding method converts the continuous intensity values (e.g., 0.831, 0.832, 0.842) into discrete integer values (e.g., 1, 1, 1), thereby creating a set of unique and distinguishable intensity values for each pixel. If the intensity values of pixels are rounded up to integers, it is possible that the size of the resulting PDF could be less than the size of the original pixels. Then, we add each PDF element and achieve the cumulative density values by the cumulative density function (CDF). This function is defined as follows:  
\[c(X_{k})=\sum_{j=0}^{k}p(X_{j})\quad for\,k=0,1,\dots,L-1. \tag{6}\]To further enhance the overall brightness and contrast of imagettes, we map the original image intensity into the range [0,\(x_{max}\)] by the transform function:  
\[f(x)=X_{max}*c(x), \tag{7}\]  
where \(x\) is the intensity of image pixels. We apply the intensity transformation function uniformly across all pixels to facilitate the mapping of the original image into a new representation characterized by the desired contrast. Our dataset comprises TenGeoP-SARvv images characterized by low contrast, frequently manifesting as dark imagery. To enhance the performance of subsequent feature extraction, we employ the aforementioned algorithms to generate brighter images.