Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

74
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
74
Manipulation and Analysis01:21

Manipulation and Analysis

23
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
23

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Comparative hyperparameter optimization of object detection models for precision monitoring of cucumber beetles and similar insects on yellow sticky cards.

Scientific reports·2026
Same author

CHUP1 restricts chloroplast movement and effector-triggered immunity in epidermal cells.

The New phytologist·2024
Same author

Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water-fat separated MRI.

Journal of medical imaging (Bellingham, Wash.)·2024
Same author

Region-based image registration for remote sensing imagery.

Computer vision and image understanding : CVIU·2023
Same author

Single slice thigh CT muscle group segmentation with domain adaptation and self-training.

Journal of medical imaging (Bellingham, Wash.)·2023
Same author

Editorial for "Diagnosis of Sarcopenia Using the L3 Skeletal Muscle Index Estimated from the L1 Skeletal Muscle Index on MR Images in Patients With Cirrhosis".

Journal of magnetic resonance imaging : JMRI·2023

相关实验视频

Updated: Jun 29, 2025

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

对基于多光谱和多时间图像的灾难映射进行概率子空间的无监督学习.

Azubuike Okorie1, Chandra Kambhamettu2, Sokratis Makrogiannnis1

  • 1Division of Physics, Engineering, Mathematics, and Computer Sciences, Delaware State University, 1200 N. DuPont Hwy, Dover, DE 19901, USA.

Machine vision and applications
|April 8, 2024
PubMed
概括

这项研究介绍了一种无监督的子空间学习方法,使用卫星图像来检测自然灾害损害. 该方法准确地识别了受损地区,有助于灾难应对和评估.

关键词:
灾难地图绘制灾难地图.图像的注册 图像的注册非参数密度估计的估计.亚空间学习是指子空间学习.

更多相关视频

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
00:07

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

8.0K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.1K

相关实验视频

Last Updated: Jun 29, 2025

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
Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
00:07

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

8.0K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.1K

科学领域:

  • 遥感 遥感 遥感 遥感
  • 地理空间分析是什么
  • 灾害管理 灾害管理

背景情况:

  • 准确识别自然灾害损害对于有效应对和尽量减少生命损失至关重要.
  • 卫星图像和遥感数据的进步使复杂的灾害监测算法成为可能.

研究的目的:

  • 开发一种无监督的子空间学习方法来识别自然灾害损坏的地区,使用多时间和多光谱卫星图像.
  • 评估该方法在各种灾害类型中的适用性,包括野火,洪水和地震/海事件.

主要方法:

  • 该方法涉及区域划分,匹配和融合.
  • 在共同的区域空间中应用无监督子空间学习来生成变化地图.
  • 概率次空间距离被用来识别受损区域,并过非灾难变化.

主要成果:

  • 该方法实现了森林火灾的平均子相似系数 (DSC) 为0.833,洪水的平均子相似系数为0.736.
  • 在地震/海事件中获得的总体DSC为0.855.
  • 与地面真相数据的验证证实了该方法的准确性.

结论:

  • 开发的无监督子空间学习方法有效地识别了多种自然灾害类型中受损的区域.
  • 高的DSC值表明强大的性能和适用于现实世界的灾难评估.
  • 这种技术为及时准确地绘制灾后损害图提供了有价值的工具.