# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 2 Dataset and Data Processing
### Data Processing  
In order to avoid overfitting in deep neural networks for oceanic phenomenon prediction in the ocean, we expand the geographic scope with the range of the operational maritime domain. In the TenGeoP-SARwv dataset of Figure 2a-j provided by the ESA, these ten grayscale images collectively encompass an extensive span of the ocean's surface, encapsulating a diverse array of features. Employing despeckling techniques such as a Lee filter, enhanced Lee filter, and supervised denoising method [20,52], these images strive to achieve an optimal equilibrium between mitigating speckle noise and retaining intricate feature details. However, it is imperative to acknowledge that the pixel intensity within these images inherently exhibits a limited range, a characteristic attributed to the underlying principles of SAR imaging system theory. This means that their features are not readily apparent because of low contrast. All original global data records are mixed in a disorderly manner. Thus, firstly, we create nested loops and conditionals to iterate over all records and judge whether the oceanic phenomenon is within the above geographic region. Each oceanic phenomenon is identified according to a specific signature or pattern. Due to the complicated nature of the C-band radar scatter response from the sea surface, it is predominantly contingent upon the incidence angle and the relative azimuthal angle between the radar system and the directional vector of the surface wind. In certain atmospheric circumstances, notably under conditions characterized by elevated wind speeds exceeding 15 m/s, the backscatter phenomenon is influenced by the combined effects of winds and oceanic wave states. As a result, these mentioned ten phenomena other than ocean waves (i.e., wind wave, swell, surface wave) may be inadequately represented or captured in the dataset. SAR images exhibit inherent statistical characteristics, and a majority of statistical models employed in their analysis have originated from the multiplicative noise model. Hence, these imagettes are characterized by the presence of multiplicative noise, which leads to variability and interference in the form of undesired fluctuations, thereby affecting the fidelity of the acquired data [53]. It is obvious that the varied noise causes rapid changes in image grayscale and serious speckle in the bright regions. To completely develop SAR image speckle, we construct a noise model and image intensity definition based on coherent radiation as follows:  
\[N(t)=B(t)*s(t), \tag{1}\] \[C(i,j)=x(i,j)*n(i,j). \tag{2}\]  
In Equation (1), the variable \(N(t)\) denotes the signal with noise embedded into it, while \(B(t)\) represents the radar backscatter property without noise for ground targets. The selection of an optimal distribution for each of these random variables relies on the types  
Figure 3: A comprehensive coverage of SAR data across diverse geographical regions for TenGeoP-SARwv dataset under VV polarization in 2016 with WV1 (**a**) and WV2 (**b**). The chromatic representation signifies the quantity of WV images contained within individual spatial bins measuring \(5^{\circ}\times 5^{\circ}\). of ground targets. The variable \(s(t)\) denotes speckle noise, which is independent of \(B(t)\). During the modeling process, we opt to model SAR image speckle noise as multiplicative noise with a mean of 1. This modeling principle ensures that the multiplicative noise does not introduce an overall bias, preserving the average brightness and intensity of the image unaffected by speckle noise. This is advantageous for various image processing tasks, allowing us to assume that the average reflection intensity remains unaltered during image processing. Thus, in Equation (1), the mean value of \(s(t)\) is one and its variance is relevant to the equivalent number of imagettes. In addition, in consideration of the multiplicative noise model, we define the intensity of an image with speckle noise by Equation (2).