Self-supervised remote sensing image denoising algorithm based on feature enhancement and contrastive learning

Models and Methods | Views:429Downloads:325CSCD:0

Export
Share
Collection
Album

Self-supervised remote sensing image denoising algorithm based on feature enhancement and contrastive learning
- role：First author第一作者
- Affiliation：
  School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
- Email：228002070392@stu.nepu.edu.cn
- Introduction：何晓晴，研究方向为遥感图像处理。E-mail： 228002070392@stu.nepu.edu.cn
HE Xiaoqing
1 * ,
- role：Corresponding author通信作者
- Affiliation：
  School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
  Bohai-Rim Energy Research Institute, Northeast Petroleum University, Qinhuangdao 066004, China
- Email：wangzhibao@nepu.edu.cn
- Introduction：王志宝，研究方向为遥感大数据。E-mail： wangzhibao@nepu.edu.cn
WANG Zhibao
12 * ,
- Affiliation：
  School of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China
ZHAO Man
3 * ,
- Affiliation：
  School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
  State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  University of Chinese Academy of Sciences, Beijing 100049, China
CHEN Liangfu
145 * ,
- Affiliation：
  School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
BI Xiuli
6 *
Vol. 30, Issue 1, Pages: 198-212(2026)
DOI：10.11834/jrs.20254566
Accepted：

Scan QR Code

Scan QR Code

Quote
Translate The Full Text
DOI：
DOI：

AI Introduction
Full Text(HTML)
Figs( 69 ) Tabs( 8 )
References
Publication Info
Metrics

Alert me when the article has been cited

Submit

Related Articles

Self-supervised monocular depth estimation in dynamic scenes based on deep learning

Monitoring and analysis of typical coastal ecological risk sources based on multi-source remote sensing： A case study of the southern coast of Yantai city

A fine-grained obect detection method for remote sensing images based on dual classification head

Hyperspectral image classification network based on multiscale supervised contrastive learning

Robust matching of optical and SAR images based on deep feature reconstruction enhancement

Related Author

CHENG Binbin 信息工程大学地理空间信息学院

YU Ying 信息工程大学地理空间信息学院

ZHANG Lei 信息工程大学地理空间信息学院

WANG Ziquan 信息工程大学地理空间信息学院

JIANG Zhipeng 信息工程大学地理空间信息学院

MA Xiaoshuang 安徽大学资源与环境工程学院;安徽大学信息材料与智能感知安徽省实验室;安徽大学湿地生态保护与修复安徽省重点实验室

HU Zhongwen 深圳大学自然资源部大湾区地理环境监测重点实验室

SONG Wenbo 安徽大学资源与环境工程学院

Related Institution

Information Engineering University, Institute of Geospatial Information

School of Resources and Environmental Engineering, Anhui University

Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University

Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University

Key Laboratory for Geo-Environmental Monitoring of Great Bay Are, Shenzhen University