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Papers
20 resultsShowing papers similar to A new band selection framework for hyperspectral remote sensing image classification
ClearMultiscale Dense Cross-Attention Mechanism with Covariance Pooling for Hyperspectral Image Scene Classification
Researchers developed a multiscale dense cross-attention mechanism with covariance pooling for hyperspectral image scene classification, addressing challenges of high dimensionality and feature redundancy in deep convolutional frameworks to improve classification accuracy.
3D Fourier-based Global Feature Extraction for Hyperspectral Image Classification
Researchers proposed HGFNet, a hybrid deep learning architecture combining 3D convolutional local feature extraction with three complementary Fourier transform strategies for hyperspectral image classification, achieving efficient long-range spectral-spatial modeling and improved discrimination of underrepresented classes via an adaptive focal loss mechanism.
Detection of Vegetation Spectral Signatures in Hyperspectral Images using Artificial Neural Networks
This study developed a computer program that can identify plants and vegetation in detailed satellite images by analyzing how they reflect different colors of light. The technology successfully detected about 42% of an area as vegetation in a test neighborhood, which was more accurate than older methods. This could help scientists better monitor environmental changes like deforestation or urban green spaces that affect air quality and human health.
Deep Kernel Extreme-Learning Machine for the Spectral–Spatial Classification of Hyperspectral Imagery
This paper describes a deep neural network method combining kernel extreme-learning machines with spectral-spatial analysis for classifying hyperspectral remote sensing images. Hyperspectral imaging is also being developed as a tool for detecting and identifying microplastics in environmental samples.
Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification
This paper presents a deep learning method for hyperspectral image classification that accounts for complex environmental variation causing within-class spectral differences. Such techniques may have applications in automated detection and identification of microplastics in environmental samples using spectral imaging.
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.
A 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.
SDRCNN: A Single-Scale Dense Residual Connected Convolutional Neural Network for Pansharpening
SDRCNN is a new single-scale lightweight convolutional neural network designed for pansharpening, fusing high-resolution panchromatic and low-resolution multispectral satellite images to produce high-resolution multispectral outputs. The architecture was designed to balance spatial and spectral quality while minimizing computational cost.
An Attention-Based 3D Convolutional Autoencoder for Few-Shot Hyperspectral Unmixing and Classification
Researchers developed a semi-supervised deep learning pipeline for hyperspectral image classification that uses a 3D convolutional autoencoder with attention mechanisms to extract spectral-spatial features, achieving better classification accuracy than supervised CNN methods when only small amounts of labeled data are available.
Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index
This paper presents a machine learning approach using neural networks to classify plant types in high-resolution hyperspectral aerial images of agricultural fields. The method could be applied to environmental monitoring, including detecting plastic contamination or pollution-induced vegetation changes in farmland.
Genetic Algorithm Based Band Relevance Selection in Hyperspectral Imaging for Plastic Waste Material Discrimination
Researchers applied a genetic algorithm to select the most relevant spectral bands from a 100-band short-wavelength infrared hyperspectral camera (1100-1650 nm) for discriminating pellet microplastic materials, demonstrating that band reduction improved cost efficiency and processing requirements without sacrificing material identification accuracy.
Enhancing the Detection of Coastal Marine Debris in Very High-Resolution Satellite Imagery via Unsupervised Domain Adaptation
Researchers proposed a satellite-based marine debris detection model using unsupervised domain adaptation to overcome limitations of applying high-resolution trained models to lower-resolution imagery. The approach improves practical applicability for monitoring coastal debris distributions across diverse satellite data sources.
Hyperspectral detection of soil microplastics via multimodal feature fusion and a dual-path attention residual convolutional network
A hyperspectral imaging approach combined with multimodal deep learning was developed to detect microplastics in soil, achieving high accuracy in identifying plastic particles against complex soil backgrounds. The method offers a faster, less destructive alternative to traditional chemical extraction and spectroscopy for soil monitoring.
Automatic Identification and Classification of Marine Microplastic Pollution Based on Deep Learning and Spectral Imaging Technology
Researchers developed an AI system combining deep learning with multispectral imaging to automatically identify and classify marine microplastics, using a feature-selection method called ReliefF to reduce noise in complex ocean samples. The approach achieved high accuracy and offers a scalable solution for large-scale ocean microplastic monitoring that outperforms traditional manual inspection.
Rapid identification of microplastics through spectral reconstruction from RGB images
Researchers developed a method to generate hyperspectral bands and extract spectral signatures from standard RGB images, applying spectral reconstruction to streamline microplastic identification. Experimental results validated the approach's efficacy in enabling comprehensive spectroscopic analysis while significantly reducing imaging time compared to traditional hyperspectral acquisition methods.
Tensor Dictionary Self-Taught Learning Classification Method for Hyperspectral Image
Researchers proposed a tensor dictionary self-taught learning method for classifying hyperspectral images when only limited training data is available. The approach uses unlabeled data to improve classification accuracy beyond what supervised methods alone can achieve. Better hyperspectral classification tools support environmental monitoring applications including detecting plastic pollution from remote sensing data.
Systematic reduction of hyperspectral images for high-throughput plastic characterization
Researchers developed a method to dramatically reduce the data size of hyperspectral images — which simultaneously capture both visual and chemical information across thousands of wavelengths — while preserving the key details needed to identify different plastic types. By removing redundant pixels and wavelengths, their approach speeds up plastic sorting analysis and makes it more practical for real-world industrial recycling facilities.
Simple and rapid detection of microplastics in seawater using hyperspectral imaging technology
Researchers developed a hyperspectral imaging technique for rapid detection and identification of microplastics in seawater, demonstrating it could analyze multiple particles simultaneously and significantly reduce the time burden compared to traditional individual-particle identification protocols.
Application of hyperspectral imaging and machine learning for the automatic identification of microplastics on sandy beaches
Hyperspectral imaging combined with machine learning was applied to identify and classify microplastics on sandy beach surfaces, offering a faster and more scalable alternative to conventional spectroscopic analysis for large-area environmental monitoring.
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.