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

Near-Infrared Polarity-Sensitive Fluorescent Probe for Distinguishing Normal and Cancer Cells and Imaging Mitochondrial Polarity Changes during Drug-Induced Liver Injury.

Analytical chemistry·2026
Same author

Near-Infrared Fluorescent Lipid Droplet Polarity Probe for Distinguishing Different Liver Injury Diseases.

Chemical & biomedical imaging·2026
Same author

Long-Term Imaging Probe for Monitoring the Changes of Cell Membrane Viscosity and Its Application in Diabetes Nephropathy.

Analytical chemistry·2026
Same author

Single-Molecule Dual-Channel NIR Fluorescence Probe for Simultaneous Monitoring ATP and ONOO<sup>-</sup> during Ferroptosis and Hepatic Ischemia-Reperfusion Injury.

Analytical chemistry·2026
Same author

Cell Membrane-Targeted Near-Infrared Fluorescent Probe for Dynamic Visualization of Brain Nitric Oxide in Epilepsy.

ACS sensors·2026
Same author

Lysosome-Targeted Dual-Functional Probe Capable of Detecting Acute Kidney Injury via In Vivo Imaging and Urinary Albumin Detection.

Analytical chemistry·2026

Related Experiment Video

Updated: Dec 31, 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

952

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High

Shiran Song1, Jianhua Liu1,2, Yuan Liu1

  • 1School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

Sensors (Basel, Switzerland)
|January 16, 2020
PubMed
Summary

This study introduces a novel deep learning approach for recognizing surface water bodies in high spatial resolution remote sensing images. The data-driven method effectively integrates bottom-up and top-down processes, improving accuracy and avoiding confusion with other features.

Keywords:
deep learninghigh spatial resolution remotely sensed imagerymulti-source and multi-temporalobject recognitionwater body

More Related Videos

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

4.3K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.5K

Related Experiment Videos

Last Updated: Dec 31, 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

952
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

4.3K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.5K

Area of Science:

  • Remote Sensing
  • Geographic Information Systems (GIS)
  • Computer Vision

Background:

  • High spatial resolution remote sensing images (HSRRSI) offer rich data for surface water body analysis.
  • Traditional methods like GEOBIA focus on bottom-up classification, neglecting top-down feedback for improved recognition.
  • Deep learning shows promise in remote sensing due to its powerful feature extraction capabilities.

Purpose of the Study:

  • To develop a novel, data-driven method for accurate water body recognition in HSRRSI.
  • To integrate bottom-up and top-down recognition processes using deep learning.
  • To provide a practical technical procedure for water body recognition in engineering applications.

Main Methods:

  • A modified Mask R-CNN deep learning framework was developed.
  • The method integrates bottom-up and top-down recognition processes.
  • The approach is entirely data-driven, requiring no prior knowledge.

Main Results:

  • The proposed method accurately recognizes multi-source and multi-temporal water bodies.
  • Experimental results demonstrate high accuracy in water body identification.
  • The approach effectively distinguishes water bodies from shadows and other ground features.

Conclusions:

  • The modified Mask R-CNN method offers a novel and effective approach for water body recognition.
  • This data-driven technique improves upon traditional methods by integrating top-down feedback.
  • The method shows significant potential for practical applications in remote sensing and water resource management.