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

You might also read

Related Articles

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

Sort by
Same author

Associations of Sleep Quality and Perceived Stress with Skin, Scalp, and Hair Health Among 1,017 Chinese Women: A Cross-Sectional Observational Study.

Clinical, cosmetic and investigational dermatology·2026
Same author

Heat-triggered phospholipid flipping stabilizes plasma membrane fluidity.

Nature·2026
Same author

Cross-Modal Graph Attention for Bridge SHM Data Imputation.

Sensors (Basel, Switzerland)·2026
Same author

ECM remodeling in the mPFC exacerbates cocaine-induced hyperactivity and impairs threat vigilance.

Translational psychiatry·2026
Same author

OsMATE16 Regulates Anther Dehiscence by Modulating IAA Content Under High Temperature.

Rice (New York, N.Y.)·2026
Same author

Increased PRSS56 expression is a causal factor and therapeutic target for human axial high myopia.

Cell research·2026

Related Experiment Video

Updated: Jul 2, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K

Siamese Transformer-Based Building Change Detection in Remote Sensing Images.

Jiawei Xiong1, Feng Liu1, Xingyuan Wang1

  • 1College of Computer Science, Xi'an Polytechnic University, Xi'an 710600, China.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
Summary

This study introduces a new Siamese transformer architecture for remote sensing building change detection. The method improves accuracy by precisely handling building boundaries and reducing false positives.

Keywords:
Siamese transformer networkbuilding change detectiondifference comparisonremote sensing image

More Related Videos

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K
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

537

Related Experiment Videos

Last Updated: Jul 2, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K
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

537

Area of Science:

  • Remote Sensing
  • Computer Vision
  • Geospatial Analysis

Background:

  • Detecting building changes in remote sensing images is challenging due to imprecise boundary data and high false-positive rates.
  • Existing methods struggle with accurately delineating building changes and require refinement.

Purpose of the Study:

  • To develop an advanced method for robust building change detection in remote sensing imagery.
  • To mitigate challenges associated with imprecise building boundaries and reduce false-positive outcomes.

Main Methods:

  • A Siamese transformer architecture incorporating a difference module was proposed.
  • A layered transformer was employed for global context modeling and multiscale feature extraction.
  • A difference module was utilized to capture pre- and post-change building features, which were then fused.

Main Results:

  • The proposed method achieved F1 scores of 89.58% on the LEVIR-CD dataset and 84.51% on the WHU-CD dataset.
  • Experimental results indicate improved robustness and detection performance in building change detection.
  • The approach effectively reduced false-positive rates in change map generation.

Conclusions:

  • The developed Siamese transformer architecture offers a significant advancement in remote sensing-based building change detection.
  • The method provides a valuable technical reference for identifying building damage and changes in remote sensing applications.
  • This research enhances the reliability of automated building change analysis using remote sensing data.