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

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

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

Sort by
Same author

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation Without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Societyยท2026
Same author

LoRASculpt: Harmonious Low-Rank Adaptation for Multimodal Large Language Models.

IEEE transactions on pattern analysis and machine intelligenceยท2026
Same author

Towards clinical-level interpretation of dental panoramic radiography using an instance-guided vision-language model.

Nature biomedical engineeringยท2026
Same author

Systemic immune-inflammation index predicts post-thrombectomy outcomes and reveals a mediating role in the association between neurocardiac stress and prognosis: a multicenter study.

Frontiers in neurologyยท2026
Same author

Holistic Invariant Retracing for Distortion-Resilient Multi-Modal Learning in Spatial Transcriptomics.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Societyยท2026
Same author

MUP-SAM: Multi-scale vision mamba UNet prompt generation for SAM in multi-organ medical image segmentation.

Neural networks : the official journal of the International Neural Network Societyยท2026
Same journal

ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion.

IEEE transactions on neural networks and learning systemsยท2026
Same journal

PIMPC-GNN: Physics-Informed Multiphase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks.

IEEE transactions on neural networks and learning systemsยท2026
Same journal

Quantum Rรฉnyi ฮฑ-Entropies for Graph Characterization.

IEEE transactions on neural networks and learning systemsยท2026
Same journal

LANet: A Lightweight and Accurate Balanced Network Based on State Space Models for Real-Time Semantic Segmentation.

IEEE transactions on neural networks and learning systemsยท2026
Same journal

MENDNet: Memory-Enhanced Dependency Network for Multistock Movement Prediction.

IEEE transactions on neural networks and learning systemsยท2026
Same journal

Temporal Mask-Embedding Learning and Query-Refined Head Network for Visual Tracking.

IEEE transactions on neural networks and learning systemsยท2026
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
07:34

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Differentiable Clustering Graph Convolutional Network for Hyperspectral Unmixing: Methodology and Benchmark.

Mingming Xu, Jin Xu, Zhiru Yang

    IEEE Transactions on Neural Networks and Learning Systems
    |June 11, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel Differentiable Clustering GCN (DCGCN) for hyperspectral unmixing (HU). The DCGCN method enhances spectral and spatial modeling accuracy by dynamically constructing graphs during training, outperforming existing techniques.

    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

    Related Experiment Videos

    Last Updated: Jun 13, 2026

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    07:34

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    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

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Machine Learning

    Background:

    • Hyperspectral unmixing (HU) is crucial for analyzing mixed pixels in hyperspectral images (HSIs).
    • Traditional methods like CNNs struggle with complex HSI data structures.
    • Existing GCNs often use static graphs, limiting adaptability.

    Purpose of the Study:

    • To develop an advanced GCN model for improved HU accuracy.
    • To address limitations of static graph structures in GCNs for HSIs.
    • To introduce a novel Differentiable Clustering GCN (DCGCN) for end-to-end HU.

    Main Methods:

    • Proposing a Differentiable Clustering GCN (DCGCN) integrating spatial information with dynamic graphs.
    • Utilizing a Differentiable Clustering Module (DCM) for automatic graph construction and updates.
    • Developing a reproducible pipeline with a new Yellow River Estuary Wetland dataset (GF-5 and GF-6 imagery).

    Main Results:

    • DCGCN demonstrates superior performance compared to state-of-the-art methods on simulated and real datasets.
    • The model achieves high accuracy and robustness in hyperspectral unmixing.
    • The new dataset provides reliable endmembers and abundances without field surveys.

    Conclusions:

    • DCGCN effectively captures complex spectral-spatial relationships in HSIs.
    • The dynamic graph approach enhances HU model flexibility and accuracy.
    • The proposed method offers a robust solution for hyperspectral unmixing challenges.