# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
### Issues  
Among the recent studies on the use of satellites to detect plastic, many mention the development of automated marine debris detection algorithms based on machine learning. As mentioned, the main obstacle for this is the lack of data: ground-truth validation of satellite data containing plastic pixels is scarce. The work of [PERSON] et al. [10] and [PERSON] et al. [11] has been instrumental in collecting this kind of validated data. Moreover, the same data have to be used for both training and testing. Thus, the risk of overfitting and poor generalization is high. As a consequence, the algorithms developed for automated plastic detection are unlikely to be robust enough for operational use. [PERSON] et al. [8] have trained a Naive Bayes classifier on 53 plastic pixels collected from the same image in Durban, 20 seawater pixels, and a few other pixels for other marine debris. [PERSON] et al. [20] have created a dataset composed of pixels of plastic extracted from the experiments in [10; 11], but in quantities close to those in [8]: 59 plastic pixels for 458 seawater pixels.  
As a follow-up to the previous work, [PERSON] et al. [21] investigated the performance of several machine learning algorithms on a larger datasets (234 plastic pixels and 234 water pixels for the largest one). Similarly, [PERSON] et al. [22] have implemented machine learning algorithms trained on pixels acquired from the same targets, but using a different satellite, PRISMA. This particular satellite has a higher resolution than S2 on the panchromatic band and significantly more spectral bands. They trained their algorithms on a pan-sharpened version of the PRISMA images.  
Even though all of the previously mentioned works obtained satisfying accuracies on plastic pixels, these promising results may be questionable in terms of generalization capability, as the amount of data used for training and testing is low. For instance, it is well known that a Naive Bayes classifier may approximate a conditional distribution under the (rather strong) hypothesis of conditional independence. However, without this assumption, the estimate with Bayes' rule of \(P(y|x)\), with \(y\) being, for instance, the fact that a pixel contains plastic or not and \(x\) a \(b\)-dimensional vector of \(n\)-ary features, i.e., the \(b\) spectral bands, each taking one of \(n\) discrete possible values, would require several training sets with at least \(2(n^{b}-1)\) samples each. Clearly, in operationally-relevant cases, \(n\) must be large, thus requiring a high number of samples for training. With more complex machine learning models, such as neural networks, the requirement on the number of training samples is more stringent [23].  
At the same time, increasing the number of plastic pixels with ground-truth validation presents challenges when it comes to rapidly iterating on machine learning algorithms, since developing measurement campaigns is expensive and time-consuming. This problem is not inherent to remote sensing in general, for instance [PERSON] and [PERSON] [24] tackle the challenge of detecting Green Plastic Cover (GPC) used for covering construction sites. In their case, their dataset contains more than 50,000 samples with 5000 of them containing GPC samples. However, as presented earlier, such amount of validated samples does not exist in our problem domain.  
[PERSON] et al. [25] have recently provided hyperspectral reflectance measurements of plastic samples in a controlled environment, simulating clear to turbid waters. This type of work paves the way for developing simulations.