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Papers
61,005 resultsShowing papers similar to SDRCNN: A Single-Scale Dense Residual Connected Convolutional Neural Network for Pansharpening
ClearA new band selection framework for hyperspectral remote sensing image classification
Researchers developed a new framework for reducing data complexity in hyperspectral satellite images by combining dual band selection with a convolutional neural network, achieving over 97% classification accuracy across three benchmark datasets. This approach could improve remote sensing applications like land cover mapping and environmental monitoring.
Multiscale 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.
From Local to Global: Efficient Dual Attention Mechanism for Single Image Super-Resolution
Researchers developed a dual attention mechanism for deep learning neural networks to improve single image super-resolution. This type of image enhancement technology could have applications in improving the detection and classification of microplastic particles in environmental images.
Applications of Deep Learning-Based Super-Resolution Networks for AMSR2 Arctic Sea Ice Images
Researchers applied deep learning-based super-resolution networks to enhance the spatial resolution of AMSR2 passive microwave satellite images of Arctic sea ice, addressing the coarse native resolution that limits detailed analysis of sea ice extent and dynamics. The approach improved image quality and enabled more precise monitoring of Arctic sea ice changes with implications for understanding climate impacts on polar ecosystems.
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.
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.
Enhancing Structural Crack Detection through a Multiscale Multilevel Mask Deep Convolutional Neural Network and Line Similarity Index
Researchers developed a multiscale multilevel mask deep convolutional neural network (MSML Mask DCNN) combined with a line similarity index (LSI) for automated structural crack detection in infrastructure. Field tests in a building and underground power tunnel demonstrated the system outperformed existing neural networks by accurately identifying linear and curvilinear crack features using only publicly available training images.
Flux to Flow: a Clearer View of Earth’s Water Cycle Via Neural Networks and Satellite Data
This dissertation developed neural network methods to enhance the spatial resolution of satellite measurements of Earth's water cycle, enabling finer-scale monitoring of hydrological processes such as precipitation, evaporation, and runoff across diverse environments.
A Hybrid Deep Learning Model for Wind and Solar Power Forecasting in Smart Grids
Researchers developed a hybrid deep learning model combining multiple neural network architectures to improve wind and solar power forecasting in smart grids, addressing limitations of traditional models in handling the complex, non-linear, and time-varying nature of renewable energy output.
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.
Development of representative convolutional neural network based models for microplastic spectral identification
Researchers developed more representative convolutional neural network (CNN) models for microplastic spectral identification by training on expanded spectral databases that include greater diversity of plastic types, aging stages, secondary additives, pigments, and environmental contamination, outperforming library-search methods in classification accuracy and speed.
Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model
Researchers evaluated deep-learning convolutional neural network approaches combined using the Dempster-Shafer model for mapping earthquake-induced landslides from PlanetScope optical and ALOS topographic data, finding that fusing two CNN streams improved mapping accuracy over single-stream approaches for post-earthquake landslide inventory generation.
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.
Enhancing marine debris identification with convolutional neural networks
A deep learning model was developed to identify and classify marine debris components captured by underwater remotely operated vehicle imagery, addressing the challenge of widely distributed ocean waste including microplastics. The convolutional neural network demonstrated improved accuracy for debris detection and classification compared to conventional image analysis methods.
Enhanced Microplastic Aggregation Prediction via Deep Learning and Spectral Analysis of Marine Snow Composition
Researchers developed a deep learning framework called the Spectral-Enhanced Aggregation Prediction Network that integrates spectral analysis of marine snow to improve prediction of microplastic aggregation rates in the ocean, addressing limitations of current models that struggle with the complex interplay of biological, chemical, and physical factors.
Extraction the Spatial Distribution of Mangroves in the Same Month Based on Images Reconstructed with the FSDAF Model
Researchers applied the FSDAF spatiotemporal fusion model to reconstruct cloud-free satellite images for the same target month, enabling accurate extraction of mangrove spatial distributions in coastal wetlands despite the persistent cloud cover that limits image availability in mangrove-growing regions. The approach demonstrated improved accuracy in mapping mangrove extent compared to methods relying on mosaicked images spanning several months.
Enhanced Microplastic Aggregation Prediction via Deep Learning and Spectral Analysis of Marine Snow Composition
Researchers developed a deep learning framework called the Spectral-Enhanced Aggregation Prediction Network that integrates spectral analysis of marine snow to improve prediction of microplastic aggregation rates in the ocean, addressing limitations of current models that struggle with the complex interplay of biological, chemical, and physical factors.
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.
Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion
A hyperspectral multi-network recognition algorithm was developed to identify ancient Sanxingdui cultural relics non-destructively, improving accuracy over conventional methods that risk damaging irreplaceable artifacts. The approach demonstrates how spectroscopic imaging and machine learning can support cultural heritage conservation.
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.
Depth-Wise Separable Convolution Attention Module for Garbage Image Classification
Researchers developed a depth-wise separable convolution attention module for classifying garbage images using deep learning. The study proposed an improved convolutional neural network architecture that enhances classification accuracy while reducing computational complexity. The findings suggest that automated image-based waste sorting using AI could improve efficiency over manual garbage classification methods.
A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification
Researchers developed a novel multi-branch channel expansion deep learning network for garbage image classification, finding that conventional deep network architectures with skip connections performed poorly on the TrashNet dataset and that their optimized structure better addressed data scarcity in this domain.
Slim Deep Learning Approach for Microplastics Image Classification in the Marine Environment
Researchers developed a lightweight convolutional neural network called the Slim-DL-Model for classifying microplastics in marine environment images, designed to overcome the computational demands of existing architectures like VGG16 and ResNet for real-time field applications. The model achieves competitive classification accuracy while significantly reducing computational requirements, enabling deployable microplastic monitoring systems.
Detection of Trash in Sea Using Deep Learning
Researchers developed a deep learning convolutional neural network (CNN) model to detect and classify trash in marine and aquatic environments from underwater images, aiming to overcome the limitations of manual debris detection for objects that may be submerged or partially obscured.