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 Experiment Videos

Frequency-Aware Refinement Network with Multi-Scale Fusion for Remote Sensing Change Detection.

Xu Zhang1, Yue Du2, Zeyu Zhang3

  • 1School of Digital Low-Altitude, Suzhou Polytechnic University, Suzhou 215104, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

You might also read

Related Articles

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

Sort by
Same author

Programmed cell death ligand 1 in correlation with <i>BRAF<sup>V600E</sup></i> mutation, molecular characteristics and biological behavior in ameloblastoma.

Frontiers in immunology·2026
Same author

Benzothiadiazole-based covalent organic frameworks with red-light emission for visual detection of norfloxacin.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

HOXC9 accelerates esophageal squamous cell carcinoma progression via OTUD1-FABP5-mediated lipid metabolic reprogramming.

Cell death & disease·2026
Same author

A Clinically Interpretable AI System for Real-Time Quality Control of Transthoracic Echocardiography: Development, Validation, and Deployment.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography·2026
Same author

Organ-on-a-chip as a next-generation tool in drug development: A bibliometric and patent analysis.

Cell transplantation·2026
Same author

Effects of a cardiometabolic risk-reducing dietary pattern health education intervention on cardiometabolic disease risk in community-dwelling older adults in China: a cluster-randomized clinical trial.

The journal of nutrition, health & aging·2026

This study introduces FARNet, a novel network for remote sensing change detection. FARNet enhances accuracy by combining frequency and RGB domain information for precise land cover variation identification.

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Remote sensing change detection (RSCD) compares bi-temporal images to identify land cover variations.
  • Conventional RSCD methods struggle with visually similar backgrounds in complex scenarios.
  • Reliance on RGB domain information limits the distinction between changed objects and background.

Purpose of the Study:

  • To develop an advanced network for accurate remote sensing change detection.
  • To overcome limitations of existing methods in complex land cover change scenarios.
  • To improve the precision and robustness of change detection using multi-domain features.

Main Methods:

  • Proposed a frequency-aware refinement network (FARNet) employing a coarse-to-fine strategy.
Keywords:
change detectioncoarse-to-finefrequency-aware refinementmulti-scaleremote sensing

Related Experiment Videos

  • Introduced a frequency-aware module (FAM) for coarse localization using frequency domain information.
  • Developed a refinement fusion module (RFM) to leverage RGB details for precise boundary refinement, incorporating edge loss.

    • Main Results:

    • FARNet effectively identifies blurred boundaries of changed objects resembling background.
    • The network refines segmentation boundaries using high-resolution RGB details for precise detection.
    • Experiments show FARNet significantly outperforms existing methods in accuracy and robustness.

    Conclusions:

    • FARNet offers a superior approach to remote sensing change detection in complex environments.
    • The integration of frequency and RGB domain information enhances detection precision.
    • The proposed method achieves state-of-the-art performance on benchmark datasets.