Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

416
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
416
Classification of Signals01:30

Classification of Signals

1.0K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sedimentary characteristics of Pu and <sup>237</sup>Np in the Pearl River Estuary under intensive land-ocean-human interactions.

Marine pollution bulletin·2025
Same author

Provenance and sedimentation of Pu and <sup>237</sup>Np in the northern Taiwan Strait suffering intensive land-ocean interaction.

Environmental pollution (Barking, Essex : 1987)·2024
Same author

Rapid Method To Determine <sup>137</sup>Cs, <sup>237</sup>Np, and Pu Isotopes in Seawater by SF-ICP-MS.

Analytical chemistry·2023
Same author

P-TransUNet: an improved parallel network for medical image segmentation.

BMC bioinformatics·2023
Same author

EG-TransUNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation.

BMC bioinformatics·2023
Same author

Spatial variation, sources, and potential ecological risk of metals in sediment in the northern South China Sea.

Marine pollution bulletin·2022

Related Experiment Video

Updated: Oct 19, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

570

Spatial-Spectral Unified Adaptive Probability Graph Convolutional Networks for Hyperspectral Image Classification.

Yun Ding, Yanwen Chong, Shaoming Pan

    IEEE Transactions on Neural Networks and Learning Systems
    |September 23, 2021
    PubMed
    Summary

    A new spatial-spectral unified adaptive probability GCN (SSAPGCN) method enhances hyperspectral image classification. It improves graph structure flexibility and feature learning through simultaneous optimization, outperforming existing methods, especially with limited training data.

    More Related Videos

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.6K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    695

    Related Experiment Videos

    Last Updated: Oct 19, 2025

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    570
    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.6K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    695

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Machine Learning

    Background:

    • Hyperspectral image (HSI) classification relies on semisupervised graph convolutional network (GCN) methods.
    • Existing GCN methods struggle with flexible graph structure encoding for spatial variability and lack feedback for optimizing graph construction.
    • These limitations lead to suboptimal performance in HSI classification tasks.

    Purpose of the Study:

    • To propose a novel spatial-spectral unified adaptive probability GCN (SSAPGCN) method for HSI classification.
    • To address the limitations of existing GCN methods in capturing spatial-spectral information and optimizing graph structures.
    • To improve the accuracy and robustness of HSI classification, particularly in complex scenarios and with limited training data.

    Main Methods:

    • Developed a spatial-spectral adaptive probability graph (SSAPG) structure by combining spectral information and spatial coordinates.
    • Integrated the SSAPG structure and GCN model into a unified framework for simultaneous adaptive learning of graph structure and output features.
    • Employed a feedback mechanism for adaptive optimization of graph construction based on output features.

    Main Results:

    • The proposed SSAPGCN method demonstrated superior performance compared to existing classification methods on four public HSI datasets.
    • Achieved higher overall accuracy (OA) and kappa coefficient (KC) metrics, indicating improved classification effectiveness.
    • Showcased particular advantages in scenarios with small training sample sizes, highlighting its robustness.

    Conclusions:

    • The SSAPGCN method effectively captures homogeneous structural similarity and probabilistic connectivity of HSI pixels.
    • The unified framework enables adaptive learning of both graph structure and features, overcoming the limitations of single-pass GCN methods.
    • SSAPGCN offers a significant advancement in HSI classification, providing a more flexible and accurate approach.