Benchmark dataset and classification of marine plastic waste acquired by a remote hyperspectral imaging system embedded on an aquatic drone.
Résumé
The problem of marine debris, particularly plastic waste, presents significant challenges for environmental pollution. This work introduces a new benchmark dataset specifically designed for the classification of floating plastic litter and captured by a remote hyperspectral imaging system embedded on an unmanned aquatic drone. This innovative system is equipped with a hyperspectral camera that covers the spectral range between 900 nm and 1700 nm, from near-infrared (NIR) to short-wave infrared (SWIR). The acquired dataset primarily focuses on various types of marine plastic debris collected from the coastline of the Côte d’Opale in France, containing seven plastic types commonly found in marine environments, in addition to a category for non-plastic debris. The hyperspectral images of this marine waste were acquired in the wave and current flume tank of IFREMER in Boulognesur-Mer. This facility is able to precisely control the speed of the water on which the debris floats and to reproduce different situations encountered in real conditions. To analyze the data, we applied several standard machine learning algorithms (Nearest Neighbor, Support Vector Machine, Random Forest, Logistic Regression, Naïve Bayes, and Linear Discriminant Analysis) as benchmarks to classify and identify debris types based on their spectral signatures. In addition, Principal Component Analysis (PCA) was applied to reduce the dimensionality of the representation space. The dataset and experimental results provide valuable resources for researchers aiming to improve marine debris detection and identification techniques using hyperspectral imaging technology.
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