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

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

110
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
110

You might also read

Related Articles

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

Sort by
Same author

Remote Sensing Inversion of Water Quality Grades Using a Stacked Generalization Approach.

Sensors (Basel, Switzerland)·2024
Same author

The purine-rich element-binding protein ChPur-α negatively regulates Hsc70 transcription in Crassostrea hongkongensis.

Cell stress & chaperones·2017
Same author

Improved antitumor effect of ionizing radiation in combination with rapamycin for treating nasopharyngeal carcinoma.

Oncology letters·2017
Same author

Roles of Cells from the Arterial Vessel Wall in Atherosclerosis.

Mediators of inflammation·2017
Same author

Metabolic and microbial signatures in rat hepatocellular carcinoma treated with caffeic acid and chlorogenic acid.

Scientific reports·2017
Same author

Arsenic removal in aqueous solution by a novel Fe-Mn modified biochar composite: Characterization and mechanism.

Ecotoxicology and environmental safety·2017

Related Experiment Video

Updated: Sep 21, 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

652

Extracting Wetland Type Information with a Deep Convolutional Neural Network.

XianMing Guan1,2,3, Di Wang4, Luhe Wan1,2

  • 1Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China.

Computational Intelligence and Neuroscience
|May 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient method using object-oriented segmentation and AlexNet deep learning for wetland remote sensing. It accurately classifies wetland types in high-resolution images, improving resource management and protection efforts.

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

Related Experiment Videos

Last Updated: Sep 21, 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

652
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

Area of Science:

  • Environmental Science
  • Remote Sensing Technology
  • Geospatial Analysis

Background:

  • Wetlands possess significant ecological value, necessitating accurate monitoring.
  • Wetland remote sensing is crucial for analyzing current conditions and resource dynamics.
  • High-resolution imagery presents challenges in distinguishing wetland types and achieving simultaneous accuracy and efficiency.

Purpose of the Study:

  • To develop an automatic and efficient method for extracting wetland type information from high-resolution remote sensing images.
  • To overcome the limitations of traditional methods in wetland classification accuracy and time efficiency.
  • To provide technical support for wetland resource protection, development, and utilization.

Main Methods:

  • Object-oriented multiscale segmentation for fine image segmentation.
  • Deep convolutional neural network (AlexNet) for automated wetland image classification.
  • Validation through a case study with field-measured data and comparison with traditional methods.

Main Results:

  • The proposed method demonstrates higher accuracy in extracting wetland types compared to traditional classification techniques.
  • The approach offers improved time efficiency for wetland type information extraction.
  • Successful classification of diverse wetland types in high-resolution remote sensing data was achieved.

Conclusions:

  • The developed method effectively addresses the bottleneck in wetland type extraction from high-resolution remote sensing data.
  • This approach enhances the accuracy and efficiency of wetland remote sensing applications.
  • The findings support the broader application of wetland remote sensing for resource management and conservation.