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Papers
61,005 resultsShowing papers similar to Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes
ClearA Combination of Machine Learning Algorithms for Marine Plastic Litter Detection Exploiting Hyperspectral PRISMA Data
Researchers applied a combination of machine learning algorithms to hyperspectral satellite imagery from the PRISMA satellite to detect marine plastic litter along coastlines and ocean surfaces. The multi-algorithm approach improved detection accuracy over single-model methods and demonstrated the potential for satellite-based monitoring of ocean plastic pollution at scale.
Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics
Researchers developed a reflectance model for how sunlight interacts with floating plastic debris on the ocean surface, accounting for plastic color, transparency, and shape, as a foundational step toward a hyperspectral remote sensing algorithm capable of detecting marine macroplastics from aircraft or satellite.
Advancing floating macroplastic detection from space using hyperspectral imagery
Researchers evaluated the use of hyperspectral satellite and airborne imagery to detect floating plastic debris in rivers and oceans, addressing major challenges related to plastic spectral properties in field conditions. Remote sensing tools for plastic detection are important for large-scale monitoring of the macro-scale plastic that eventually becomes microplastics.
Abundance of Plastic-Litter in Hyperspectral Imagery Using Spectral Unmixing in Coastal Environment
This study tested whether hyperspectral satellite or aerial imagery combined with spectral unmixing algorithms can detect and map microplastic litter in coastal environments. Results showed the approach can identify plastic fragments smaller than a pixel by analyzing mixed spectral signals, offering a scalable monitoring tool. Remote sensing methods like this could greatly reduce the cost and labor of tracking coastal plastic pollution at large spatial scales.
Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms
This paper is not primarily about microplastics; it focuses on hyperspectral band-selection algorithms to identify the optical spectral signatures of plastic litter under water, primarily as a remote-sensing detection methodology. While relevant to plastic pollution monitoring, it does not assess microplastic abundance, distribution, or ecological/health effects.
Hyperspectral ultraviolet to shortwave infrared characteristics of marine-harvested, washed-ashore and virgin plastics
Researchers characterized the hyperspectral optical properties of marine-harvested plastic litter across ultraviolet to shortwave infrared wavelengths, generating spectral signatures needed to support remote sensing detection of floating plastic debris. The spectral library produced contributes to developing satellite and airborne monitoring systems for large-scale ocean plastic surveillance.
Investigating Detection of Floating Plastic Litter from Space Using Sentinel-2 Imagery
Researchers tested whether Sentinel-2 satellite imagery could detect floating plastic debris on the ocean surface, using a 3 by 10 meter plastic bottle target deployed off Cyprus. A newly developed Plastic Index proved more effective than existing indices at identifying the target, offering a promising tool for large-scale ocean plastic monitoring from space.
On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring
This review evaluates the current state of satellite and aerial remote sensing for detecting marine plastic debris, noting that while progress has been made using optical and hyperspectral imaging, significant challenges remain including low detection resolution for small particles, confusion with other floating materials, and the need for better machine learning algorithms. The paper is relevant to the microplastics field as large-scale monitoring tools are needed to track plastic pollution distribution and inform cleanup and policy efforts, though direct detection of microplastics (<5 mm) from orbit remains largely out of reach with current technology.
Hyperspectral Imaging and Data Analysis for Detecting and Determining Plastic Contamination in Seawater Filtrates
Researchers tested whether hyperspectral imaging combined with multivariate data analysis could detect and identify plastic particles on filters from seawater samples, finding the method could locate plastic contamination and distinguish polymer types. This approach could offer a faster and more automated alternative to manual microscopy for environmental microplastic monitoring.
Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery
Researchers tested experimental hyperspectral airborne imagery to detect floating macroplastics in rivers and the ocean, demonstrating that combining spectral and spatial features improves detection accuracy over single-band approaches.
Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
Researchers explored the use of hyperspectral imaging technology to detect and identify different types of plastic debris on beach sand. The method can distinguish between various polymer types, supporting more efficient recycling and cleanup operations. The study demonstrates a non-contact detection approach that could help prevent further degradation of shoreline plastics into microplastics.
Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis
Researchers analyzed shortwave infrared reflectance spectra of weathered marine plastic debris on sandy beaches, finding that polymer type significantly influences detection capability at sub-pixel surface covers relevant to remote sensing applications.
Marine Microplastic Classification by Hyperspectral Imaging: Case Studies from the Mediterranean Sea, the Strait of Gibraltar, the Western Atlantic Ocean and the Bay of Biscay
Using advanced hyperspectral imaging across water samples from four ocean regions, researchers characterized the polymer types and physical dimensions of collected microplastics, finding polyethylene, polypropylene, polystyrene, and expanded polystyrene as the most common materials. Hyperspectral imaging can analyze many particles quickly and simultaneously capture morphological data, making it a powerful tool for large-scale environmental monitoring programs.
Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)
Researchers tested remote sensing of floating plastic targets in a real marine environment using Sentinel-2 satellite imagery and unmanned aerial systems during the 2019 Plastic Litter Project, collecting reference spectral data to help calibrate detection algorithms. The study provided a validated dataset characterizing the spectral behavior of floating plastics to support future remote monitoring efforts.
An effective strategy for the monitoring of microplastics in complex aquatic matrices: Exploiting the potential of near infrared hyperspectral imaging (NIR-HSI)
Researchers developed a near infrared hyperspectral imaging (NIR-HSI) method for rapid monitoring of microplastics in complex marine matrices, demonstrating effective detection and polymer identification that overcomes the time and cost limitations of conventional spectroscopic analysis approaches.
Hyperspectral reflectance of pristine, ocean weathered and biofouled plastics from a dry to wet and submerged state
Researchers built an open-access hyperspectral library covering pristine, ocean-weathered, and artificially biofouled plastics measured from dry through submerged states, filling a gap in reference data needed for satellite and drone-based plastic pollution monitoring. The library is particularly valuable because biofouling alters a plastic's optical signature and makes remote identification much harder, so having reference spectra for fouled materials improves algorithm accuracy for detecting plastic debris in real-world ocean environments.
Characterization of microplastic litter from oceans by an innovative approach based on hyperspectral imaging
Hyperspectral imaging was developed as an innovative method for characterizing marine microplastic litter collected from diverse ocean regions including the Arctic and Mediterranean, enabling rapid spectral mapping of polymer composition across samples. The approach offers a high-throughput alternative to conventional spectroscopic methods for analyzing large numbers of environmental microplastic samples.
Hyperspectral remote sensing as an environmental plastic pollution detection approach to determine occurrence of microplastics in diverse environments
Researchers tested whether hyperspectral remote sensing technology could detect microplastics mixed into different environmental surfaces like soil, water, concrete, and vegetation. Using near-infrared and short-wave infrared imaging, they achieved over 90% accuracy in detecting and classifying six common plastic types at concentrations as low as 0.15%. The study suggests that remote sensing could become a practical, large-scale tool for monitoring microplastic pollution across diverse environments.
Towards the Spectral Mapping of Plastic Debris on Beaches
This paper reviews the use of remote sensing (satellite and aerial imaging) to detect and map plastic debris on beaches. Advances in spectral imaging could allow large-scale automated monitoring of coastal plastic pollution, which is currently labor-intensive and limited in coverage.
Indoor spectroradiometric characterization of plastic litters commonly polluting the Mediterranean Sea: toward the application of multispectral imagery
Researchers used a laboratory spectrometer to measure the light reflectance of common plastic types found in the Mediterranean Sea as a step toward developing remote sensing methods to detect marine plastic pollution from satellites or aircraft. Aerial monitoring of plastic pollution could revolutionize our ability to track and manage large-scale ocean plastic contamination.
A Preliminary Study on the Utilization of Hyperspectral Imaging for the On-Soil Recognition of Plastic Waste Resulting from Agricultural Activities
Researchers explored the use of near-infrared hyperspectral imaging to detect and identify plastic waste in agricultural soils. They developed a classification model that could distinguish different types of plastic from soil and assess the degradation state of the material. The study demonstrates that hyperspectral imaging combined with chemometric analysis offers a rapid, non-destructive approach for monitoring plastic contamination in agricultural environments.
Finding Plastic Patches in Coastal Waters using Optical Satellite Data
Researchers demonstrated for the first time that floating macroplastic patches can be detected in optical data from the European Space Agency's Sentinel-2 satellites, validating detections against ground-truth observations and identifying characteristics that distinguish plastic from other floating material.
Rapid shipboard measurement of net-collected marine microplastic polymer types using near-infrared hyperspectral imaging
Researchers developed a rapid near-infrared hyperspectral imaging method for identifying polymer types in ship-collected marine microplastic samples, achieving results in minutes compared to hours for conventional methods and enabling higher-throughput ocean monitoring.
Spectrometric Detection Of Microplastics In The Environment: A Novel Approach Using Hyperspectral Imaging System
This study developed a novel spectrometric approach to detect microplastics in environmental samples, combining spectral analysis with machine learning classification. The method enabled rapid, accurate identification of multiple polymer types without extensive sample preparation.