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

Light Acquisition02:16

Light Acquisition

8.7K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.7K

You might also read

Related Articles

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

Sort by
Same author

LaVIDE: Language-Prompted Satellite Change Detection via Map-Image Alignment.

IEEE transactions on pattern analysis and machine intelligenceยท2026
Same author

EVDI++: Event-based Video Deblurring and Interpolation via Self-Supervised Learning.

IEEE transactions on pattern analysis and machine intelligenceยท2026
Same author

Interacted Planes Reveal 3D Line Mapping.

IEEE transactions on pattern analysis and machine intelligenceยท2026
Same author

Rejoining fragmented ancient bamboo slips with physics-driven deep learning.

Nature communicationsยท2026
Same author

Understanding Data Influence With Differential Approximation.

IEEE transactions on pattern analysis and machine intelligenceยท2026
Same author

Revisiting Fine-Grained Image Analysis by Semantic-Part Alignment.

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

Related Experiment Video

Updated: Oct 29, 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

728

Local Semantic Enhanced ConvNet for Aerial Scene Recognition.

Qi Bi, Kun Qin, Han Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 8, 2021
    PubMed
    Summary

    This study introduces LSE-Net, a novel deep learning model for aerial scene recognition. LSE-Net effectively identifies key local regions, improving classification accuracy by mimicking human visual perception.

    More Related Videos

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.1K

    Related Experiment Videos

    Last Updated: Oct 29, 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

    728
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.1K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Remote Sensing

    Background:

    • Aerial scene recognition is complex due to intricate object arrangements in large-scale images.
    • Existing deep learning models struggle to effectively perceive crucial local regions for accurate classification.

    Purpose of the Study:

    • To develop a Local Semantic Enhanced ConvNet (LSE-Net) for improved aerial scene recognition.
    • To mimic human visual perception for building discriminative local semantic representations.

    Main Methods:

    • Designed a multi-scale dilated convolution operator to fuse multi-level, multi-scale features.
    • Introduced a two-branch local semantic perception module with context-aware class peak response (CACPR) measurement.
    • Extracted a spatial attention weight matrix to denote the importance of key local regions.

    Main Results:

    • LSE-Net demonstrated state-of-the-art performance on three aerial scene classification benchmarks.
    • The proposed local semantic perception module and CACPR measurement proved effective.
    • Achieved superior accuracy in aerial image classification tasks.

    Conclusions:

    • LSE-Net offers a significant advancement in aerial scene recognition.
    • The model's ability to perceive key local regions enhances classification performance.
    • The CACPR measurement is a valuable tool for analyzing visual importance in aerial imagery.