Abstract
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.
Highlights
Large marine litter accumulation zones, such as marine litter windrows [1], are appearing in the oceans, such as Atlantic [2] and Pacific [3,4]
An important research vector is in the detection and monitoring of the marine litter accumulation zones
We extend previous work by developing, training, and testing supervised machine learning methods (RF, Support Vector Machines (SVM)) to detect and classify marine litter samples based on the remotely acquired hyperspectral flight data
Summary
Large marine litter accumulation zones, such as marine litter windrows [1], are appearing in the oceans, such as Atlantic [2] and Pacific [3,4]. An important research vector is in the detection and monitoring of the marine litter accumulation zones. Since the marine litter accumulation zones are spread over large areas, Earth observation satellite-based technological resources are currently being employed to detect and monitor marine litter. These efforts are still in the early stage of their development and still require further experimental validation, using complementary detection solutions, whether using remote and/or in-situ observations
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