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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

285
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
285
Aggregates Classification01:29

Aggregates Classification

581
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...
581
Deconvolution01:20

Deconvolution

432
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
432
Force Classification01:22

Force Classification

2.1K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Scale-Aware Prompting With Optimal Transport for Remote Sensing Image Captioning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

DI3CL: Contrastive Learning With Dynamic Instances and Contour Consistency for SAR Land-Cover Classification Foundation Model.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Bisphenol A Promotes Ovarian Cancer Proliferation and Migration through the HK2/H3K18la/IGF2BP3 Sequential Regulatory Axis.

Journal of agricultural and food chemistry·2026
Same author

A novel vascularized hydrogel by encapsulation of lyophilized platelet-rich fibrin into gelatin methacryloyl hydrogel for bone regeneration.

Journal of materials science. Materials in medicine·2025
Same author

Engineering a Hydrogen Peroxide-Activated Hydrogen Sulfide Donor-Based Fluorescent Agent for Integrated Diagnosis and Therapy of Chronic Wounds.

ACS sensors·2025
Same author

Metformin Attenuates Myocardial Ischemia-Reperfusion Injury in Rats by Modulating JNK Pathway and Inhibiting PANoptosis Mechanisms.

Cardiovascular therapeutics·2025

Related Experiment Video

Updated: Dec 2, 2025

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

849

Deep Multiview Union Learning Network for Multisource Image Classification.

Xu Liu, Licheng Jiao, Lingling Li

    IEEE Transactions on Cybernetics
    |November 5, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a deep multiview union learning network (DMULN) for multisensor image classification. Integrating multisource information significantly enhances classification performance.

    More Related Videos

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

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    666

    Related Experiment Videos

    Last Updated: Dec 2, 2025

    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

    849
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    666

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Machine Learning

    Background:

    • Multisensor image classification is crucial for image interpretation.
    • Existing methods face challenges in effectively utilizing diverse sensor data.

    Purpose of the Study:

    • To propose a novel deep multiview union learning network (DMULN) for multisensor image classification.
    • To enhance classification performance by effectively fusing information from multiple sources.

    Main Methods:

    • Designed an associated feature extractor using canonical correlation analysis (CCA).
    • Employed a two-branch deep learning architecture for high-level feature extraction.
    • Introduced a novel view union pooling method for multiview feature fusion.
    • Developed an end-to-end trainable network for efficient optimization.

    Main Results:

    • The DMULN achieved comparable results on IEEE_grss_dfc_2017 and IEEE_grss_dfc_2018 datasets.
    • Demonstrated that leveraging abundant multisource information improves classification accuracy.
    • The end-to-end framework proved easy to optimize.

    Conclusions:

    • The proposed DMULN effectively classifies multisensor data by integrating multiview features.
    • Multisource information fusion is a key factor in improving image classification performance.
    • The DMULN offers a promising approach for advanced remote sensing image analysis.