海洋测绘2024，Vol.44Issue(1)：63-67,5.DOI:10.3969/j.issn.1671-3044.2024.01.013
基于密集多尺度特征的遥感影像水体提取
Water extraction based on dense multi-scale features from remote sensing images
马天浩 1杨海成 1李云涛 1梁四幺 1王晗1
作者信息
- 1. 核工业航测遥感中心,河北石家庄 050011||中核三维地理信息工程技术研究中心,河北石家庄 050011
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摘要
Abstract
Aiming at the problem of loss of edge detail information and low accuracy in the extraction results of traditional remote sensing image water extraction methods and classical target extraction models based on deep learning,this paper proposes a multi-scale feature dense connection network structure based on deep feature coding and water recognition decoding.Firstly,the ordinary convolution in the deep feature coding structure is used to extract the feature information of the water body in the image,then the dense multi-scale feature module is used to extract the multi-scale features of the water body and retain the global information,and finally the water body in the image is predicted by the water body recognition and decoding structure.Experimental results show that the extraction accuracy of the proposed method is superior to the classical full convolutional neural network model.The pixel accuracy on the test set reaches 98.56％and the intersection over Union reaches 78.91％,effectively preserving the integrity of the water body and the detailed edge information,and realizing the fine extraction of the water body.关键词
遥感影像/深度学习/水体提取/密集连接网络/膨胀卷积/密集多尺度特征Key words
remote sensing image/deep learning/water extraction/dense connection network/expansion convolution/dense multi-scale features分类
天文与地球科学引用本文复制引用
马天浩,杨海成,李云涛,梁四幺,王晗..基于密集多尺度特征的遥感影像水体提取[J].海洋测绘,2024,44(1):63-67,5.
