High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing

An automatic custom-made procedure is developed to identify macroplastic debris loads in coastal and marine environment, through hyperspectral imaging from unmanned aerial vehicles (UAVs). Results obtained during a remote-sensing field campaign carried out in the seashore of Sassari (Sardinia, Italy) are presented. A push-broom-sensor-based spectral device, carried onboard a DJI Matrice 600 drone, was employed for the acquisition of spectral data in the range 900−1700 nm. The hyperspectral platform was realized by assembling commercial devices, whereas algorithms for mosaicking, post-flight georeferencing, and orthorectification of the acquired images were developed in-house. Generation of the hyperspectral cube was based on mosaicking visible-spectrum images acquired synchronously with the hyperspectral lines, by performing correlation-based registration and applying the same translations, rotations, and scale changes to the hyperspectral data. Plastics detection was based on statistically relevant feature selection and Linear Discriminant Analysis, trained on a manually labeled sample. The results obtained from the inspection of either the beach site or the sea water facing the beach clearly show the successful separate identification of polyethylene (PE) and polyethylene terephthalate (PET) objects through the post-processing data treatment based on the developed classifier algorithm. As a further implementation of the procedure described, direct real-time processing, by an embedded computer carried onboard the drone, permitted the immediate plastics identification (and visual inspection in synchronized images) during the UAV survey, as documented by short video sequences provided in this research paper.

[1]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Richard C. Thompson,et al.  The impact of debris on marine life. , 2015, Marine pollution bulletin.

[3]  J. Arnó,et al.  Review. Precision Viticulture. Research topics, challenges and opportunities in site-specific vineyard management , 2009 .

[4]  Richard C. Thompson,et al.  Toward the Integrated Marine Debris Observing System , 2019, Front. Mar. Sci..

[5]  M. Balsi,et al.  A UAV-Based Thermal-Imaging Approach for the Monitoring of Urban Landfills , 2020, Inventions.

[6]  Lonneke Goddijn-Murphy,et al.  Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics. , 2018, Marine pollution bulletin.

[7]  Vegetation monitoring via a novel push-broom-sensor-based hyperspectral device , 2019, Journal of Physics: Conference Series.

[8]  Alessandro Mei,et al.  PET and PVC Separation with Hyperspectral Imagery , 2015, Sensors.

[9]  Y. Shimabukuro,et al.  Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data , 2016 .

[10]  Charles K. Toth,et al.  Remote sensing platforms and sensors: A survey , 2016 .

[11]  Andrea Berton,et al.  Forestry applications of UAVs in Europe: a review , 2017 .

[12]  Stefan G. H. Simis,et al.  Measuring Marine Plastic Debris from Space: Initial Assessment of Observation Requirements , 2019, Remote. Sens..

[13]  D. Wienke,et al.  Plastic material identification with spectroscopic near infrared imaging and artificial neural networks , 1998 .

[14]  Manuel Arias,et al.  Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019) , 2020, Remote. Sens..

[15]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[16]  C. Mattar,et al.  Hyperspectral longwave infrared reflectance spectra of naturally dried algae, anthropogenic plastics, sands and shells , 2020 .

[17]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[18]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[19]  A. Goetz,et al.  Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean , 2009 .

[20]  Antonio Cenedese,et al.  Mosaicing of Hyperspectral Images: The Application of a Spectrograph Imaging Device , 2012, Sensors.

[21]  Joanne C. White,et al.  Remote Sensing Technologies for Enhancing Forest Inventories: A Review , 2016 .

[22]  Konstantinos N. Topouzelis,et al.  Detection of floating plastics from satellite and unmanned aerial systems (Plastic Litter Project 2018) , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[23]  G. Tanda,et al.  Use of multispectral and thermal imagery in precision viticulture , 2019, Journal of Physics: Conference Series.

[24]  Heidi M. Dierssen,et al.  Sensing Ocean Plastics with an Airborne Hyperspectral Shortwave Infrared Imager. , 2018, Environmental science & technology.