Automatic Detection and Identification of Floating Marine Debris Using Multispectral Satellite Imagery

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; pp. 1 - 15
• Main Authors: Duarte, Miguel M., Azevedo, Leonardo
• Format: Journal Article
• Language: English
• Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
• Subjects: Accuracy; Algae; Band spectra; Classification; Datasets; Detritus; Driftwood; Environmental impact; Floating; Generative adversarial networks; Marine debris; Oceans; Organic matter; Pixels; Plastic debris; Pumice; Satellite imagery; Seaweeds; Spectral bands; Synthetic data; Uncertainty
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3283607

Floating plastic debris represent an environmental threat to the maritime environment as they drift the oceans. Developing tools to detect and remove them from our oceans is critical. We present an approach to detect and distinguish suspect plastic debris from other floating materials (i.e., driftwood, seaweed, sea snot, sea foam, and pumice) using Sentinel-2 data. We use extreme gradient boosting trained with data compiled from published works complemented by manual interpretation of satellite images. The method is trained with two spectral bands and seven spectral indices computed from the Sentinel-2 spectral bands. We consider three application scenarios. The first uses the database created under the scope of this work. While the classification achieved a 98% accuracy rate for suspect plastic debris, we acknowledge the need for ground-truth validation. The second, to enlarge the training dataset, uses synthetic data generated through a Wasserstein generative adversarial network. A supervised model trained exclusively with synthetic data successfully classified suspect plastic pixels with an accuracy of 83%. The third comprises an ensemble model that quantifies uncertainty about the predictions obtained with the classifier. We correctly classified 75% of the suspect plastic pixels. However, while the classification accuracy decreased, with the integration of uncertainty in the predictions, the number of misclassifications also significantly decreased when compared to the model with the highest accuracy of the previous scenarios. Due to the mixed band nature of the sensor and subpixel coverage of debris within a pixel, the application to other datasets might not be straightforward.