# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
## 3 Results on Real Images  
We tested the classifier developed in Section 2.2.6 on real images, i.e., S2 images containing plastic and wood patches. We collected four images from the different PLP experiments. One contains a plastic target of 10 m \(\times\) 10 m, one contains a plastic target of 28 m diameter and the last two contain two 28 m diameter targets, one of plastic and one of wood. All of the images are described in Table 3. We collected only level 2A images, i.e., atmospheric corrected images, in accordance with our model (see Hypothesis H3 in Section 2). These images were chosen due to the large size of some of the targets (which should ensure that the aliasing artifacts are not too severe), the presence of multiple materials and the different conditions in which they were taken (for example, in image 4 of Figure 11, the targets are biofouled). Figure 11 presents the results obtained. The images collected from S2 are displayed on the first row for B02 and on the second row are the predicted heatmaps (see legend). The True Positive (TP) and False Positive (FP) values for plastic and wood pixels are presented in Tables 4 and 5. On images 2, 3, and 4, the classifier correctly detected seawater, plastic, and wood patches. However, on image 1, it only predicted plastic. In order to better understand the  
Figure 10: Ratio of the number of plastic pixels detected on the number of plastic pixels simulated depending on the radius of the patch and for 10 different jitters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Location** & **Image Filename** & **Date of Acquisition** & **Description** \\ \hline
**1** & Mytilene, Greece & S2A\_MSII2A\_20180607T085601\_N00208\_RO07\_T355 MD\_20180607T114919 & 7 July 2018 &
\begin{tabular}{c} 10 m \(\times\) 10 m target \\ of plastic bottles \\ \end{tabular} \\ \hline
**2** & Gulf of Gera, Greece & S2A\_MSII2A\_20210611T085601\_N0300\_RO07\_T355 MD\_20210611T121904 & 11 June 2021 & 28 m diameter target \\ \hline
**3** & Gulf of Gera, Greece & S2A\_MSII2A\_2021070T01085601\_N03001\_RO07\_T355 MD\_2021070T1T25029 & 1 July 2021 & 1 July 2021 &
\begin{tabular}{c} 28 m diameter target \\ HDPE mesh and wood \\ \end{tabular} \\ \hline
**4** & Gulf of Gera, Greece & S2A\_MSII2A\_20210721T085601\_N0301\_RO07\_T355 MD\_20210721T121035 & 21 July 2021 &
\begin{tabular}{c} two 28 m diameter targets \\ HDPE mesh and wood biofouled \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Table describing the images collected from S2 containing plastic patches. cause of this mis-classification, we tested the classifier on another image from the PLP21 from the \(11\) June with the same scene as the one in image 2. For this image, the results obtained were the same as the ones obtained for image 1, i.e., most of the seawater pixels were classified as plastic. The common point to these 2 images are the seawater reflectance values: they were significantly higher than the seawater reflectance values we modeled and generated. Figure 12 shows the 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 in dark blue. 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). We can see that, for image 1 and for the image of the \(11\) June (on the top of Figure 12), the real seawater values are higher than regular seawater values and overlap with the simulated plastic values. In this case, the spectral signature of simulated water does not meet the real seawater values for most bands.