Review of bitemporal change detection using SAR imagery

Review | Views:1047Downloads:2471CSCD:0

Export
Share
Collection
Album

Review of bitemporal change detection using SAR imagery
“New breakthrough in remote sensing monitoring: Synthetic Aperture Radar (SAR) has the ability to image 24/7, and the team has reviewed hundreds of literature to establish a dual phase detection system, providing solutions for disaster assessment and resource management”
- role：First author第一作者
- Affiliation：
  School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
- Email：202311070410@std.uestc.edu.cn
- Introduction：刘玉婷，研究方向为土地利用变化检测。E-mail： 202311070410@std.uestc.edu.cn
LIU Yuting
1 * ,
- role：Corresponding author通信作者
- Affiliation：
  School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
  The Innovation Center for Technological Engineering of Ecological Protection, Restoration, and Comprehensive Improvement of Land and Space in Southwest China, Ministry of Natural Resources, Chengdu 610045, China
- Email：lishihua@uestc.edu.cn
- Introduction：李世华，研究方向为农业遥感。E-mail： lishihua@uestc.edu.cn
LI Shihua
12 * ,
- Affiliation：
  School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
HE Ze
1 * ,
- Affiliation：
  School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
LIU Kaitong
1 *
Vol. 30, Issue 1, Pages: 11-41(2026)
DOI：10.11834/jrs.20255072
Accepted：

Scan QR Code

Scan QR Code

Quote
Translate The Full Text
DOI：
DOI：

AI Introduction
Full Text(HTML)
Figs( 8 ) Tabs( 13 )
References
Publication Info
Metrics

Alert me when the article has been cited

Submit

Related Articles

Research on dust detection method using U-Net with Spatial-Spectral Attention mechanism： A case study based on FY-4A satellite data

Small target detection method for remote sensing images with multi-attention mechanism

Mamba-CSNet： Joint CNN and Mamba State-Space Modeling for Hyperspectral Image Compressive Sensing

Multimodal large language model-driven landslide reasoning

A high-resolution remote sensing cropland field parcel dataset for the middle Yangtze River plain region

Related Author

FENG Zixian 宁夏大学地理科学与规划学院;宁夏驭星属陈航天科技有限公司

SHE Lu 宁夏大学地理科学与规划学院

YANG Junming 宁夏大学地理科学与规划学院

YAN Chen 宁夏大学地理科学与规划学院

ZHAO Honghong 宁夏大学地理科学与规划学院

XIAO Zhenjiu 辽宁工程技术大学软件学院

ZHAO Zhihao 辽宁工程技术大学软件学院

QU Haicheng 辽宁工程技术大学软件学院

Related Institution

School of Geography and Planning, Ningxia University

Ningxia Yuxing Shuchen Aerospace Technology Co., Ltd.

School of Software, Liaoning Technical University

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University

School of Artificial Intelligence, Wuhan University