# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
## 4 Discussion  
The satellite model was developed based on hypotheses, that we discuss now. For instance, to simplify, we neglected the effects of the atmosphere on the reflectance (H3 in Section 2.1.2). The images obtained by our simulation are thus comparable to level 2A S2 products i.e., atmospheric corrected images, assuming this correction is perfect. These level 2A images are obtained after processing level 1C images by a default atmospheric correction processor called Sen2 cor, which was developed by ESA. However, some studies question the Sen2 Cor algorithm: [10] showed that Sen2 Cor produces higher reflectance values for water constituents, which could interfere with floating plastics detection. An alternative that has been used in some studies is the ACOLITE algorithm [20]. It would be interesting to add the effects of atmosphere on the reflectance in order to generate images closer to S2 level 1C products. When testing the classifier on real images, we noticed that our model of seawater should be improved, as this seems to be a major driver of a robust performance. For now, we load the spectral signature of seawater from the USGS library [35] but this not diverse enough. Particularly, they provide with spectral signatures for deep ocean and coastal  
Figure 12: Boxplots of the simulated plastic values and real seawater values. For both figures, the transparent boxplots contain simulated plastic values taken from our dataset and the averaged spectral signature of simulated water, the dark blue line. For both figures, the blue boxplots contain real seawater values. On the top, the real seawater values are collected from image 1 and from the image of the 11 June (600 seawater pixels). On the bottom, the real seawater values are collected from image 2, 3, and 4 (1200 pixels). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image ID** & **Number of Wood Pixels** & **TP** & **FP** \\ \hline
**3** & 12 & 11 & 0 \\ \hline
**4** & 14 & 9 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Table with the true and false positive numbers for S2 images 2 and 3.  
waters, but the values are exactly the same. Collecting more spectral data on seawater under various conditions (different wave types and amplitudes, different viewing and lighting angles, etc.) will enable the development of more realistic seawater models. Another phenomenon that could lead to distorted reflectance values is sun glint-strong specular highlights on water. In particular cases, the radiances measured by the satellite from seawater could be significantly higher, leading to mis-predictions. Modeling sun glint could increase the robustness of classifiers in operating conditions. Another option would be to attempt to rectify the real images before predicting on them by using techniques such as those presented in [42]. Currently, as stated in Hypothesis H5, we do not take into account the positions of the Earth and Sun, while a more realistic model would be harder to define, taking spatial parameters into account would allow the generation of more varied datasets. Accurate modeling of the Sun-Earth-Satellite system could also simplify the task of modeling sun glint. Although our implementation of the framework is currently configured to generate images that attempt to mimic those produced by S2, this can be easily adapted. Products from other satellites can be simulated by changing the definitions of the bands: wavelength, sensitivity, PSF, and resolution. New materials can be easily added and existing materials can be modified by changing values in the material database. It would also be easy to generalize the shape parametrization to any polygon. The noise model can be adapted by changing the \(\sigma\) value or even the distribution. Adjustment of the noise could force classifiers to be less dependent on the conditions imposed by our hypotheses and thus be more likely to generalize well. We conceived the simulator specifically so that it could be used in other situations. See Section 2.2.1 for instructions on how to access the code.