| 注册
首页|期刊导航|人民珠江|基于分层特征提取和多尺度特征融合的高分辨率遥感影像水体提取深度学习算法

基于分层特征提取和多尺度特征融合的高分辨率遥感影像水体提取深度学习算法

盛晟 万芳琦 林康聆 胡朝阳 陈华

人民珠江2024,Vol.45Issue(2):45-52,8.
人民珠江2024,Vol.45Issue(2):45-52,8.DOI:10.3969/j.issn.1001-9235.2024.02.006

基于分层特征提取和多尺度特征融合的高分辨率遥感影像水体提取深度学习算法

Deep Learning Algorithm for Water Body Extraction from High-resolution Remote Sensing Images Based on Hierarchical Feature Extraction and Multi-scale Feature Fusion

盛晟 1万芳琦 2林康聆 1胡朝阳 3陈华1

作者信息

  • 1. 武汉大学水资源工程与调度全国重点实验室,湖北 武汉 430072
  • 2. 江西省自然资源测绘与监测院,江西 南昌 330009
  • 3. 福建省水利水电勘测设计研究院,福建 福州 350001
  • 折叠

摘要

Abstract

Highly accurate water body extraction can be helpful for water resources monitoring and management.The current methods of water body extraction based on remote sensing images lack attention to boundary quality,resulting in inaccurate boundary delineation and low detail retention.To improve the boundary and detail accuracy of water body extraction for remote sensing images,this paper proposes a deep learning algorithm for water body extraction from high-resolution remote sensing images based on multi-scale feature fusion.The model includes a hierarchical feature extraction module and a stacked-connected decoder module that fuses multi-scale features.In the hierarchical feature extraction module,a channel attention structure is introduced for integrating shape,texture,and hue information of water bodies in high-resolution remote sensing images to better understand the shapes and boundaries of water bodies.In the stacked-connected decoder module that incorporates multi-scale features,the stacked connection of multi-level semantic information and enhanced feature extraction are performed.Meanwhile,broad background information and fine detail information are captured to achieve better water body extraction results.Experimental results on both self-annotated and publicly available datasets show that the model yields 98.37%and 91.23%accuracy,and extracts more complete edges of water bodies while retaining more details than existing semantic segmentation models.The proposed model improves the accuracy and generalization ability of water body extraction and provides references for water body extraction from high-resolution remote sensing images.

关键词

水体提取/高分辨率遥感影像/深度学习/多尺度特征融合

Key words

water body extraction/high-resolution remote sensing images/deep learning/multi-scale feature fusion

分类

天文与地球科学

引用本文复制引用

盛晟,万芳琦,林康聆,胡朝阳,陈华..基于分层特征提取和多尺度特征融合的高分辨率遥感影像水体提取深度学习算法[J].人民珠江,2024,45(2):45-52,8.

基金项目

国家重点研发计划项目(2022YFC3002701) (2022YFC3002701)

人民珠江

1001-9235

访问量0
|
下载量0
段落导航相关论文