多注意力融合的环高原湖泊遥感影像分割

何自芬; 史本杰; 张印辉; 李素敏

doi:10.12263/DZXB.20220085

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多注意力融合的环高原湖泊遥感影像分割

学术论文 | 更新时间：2025-12-08

- 多注意力融合的环高原湖泊遥感影像分割
- Remote Sensing Image Segmentation of Around Plateau Lakes Based on Multi-Attention Fusion
- 电子学报 2023年51卷第4期页码：885-895
- 作者机构：
  
  1.昆明理工大学机电工程学院，云南昆明 650500
  2.昆明理工大学国土资源工程学院，云南昆明 650500
- 作者简介：
  
  [ "何自芬女，1976年10月出生于河北省南宫市.现为昆明理工大学机电工程学院副教授、硕士生导师.主要研究方向为计算机视觉、图像处理.E-mail: zyhhzf1998@163.com" ]
  [ "史本杰男，1997年10月出生于四川省成都市.现在为昆明理工大学硕士研究生.主要研究方向为遥感图像处理.E-mail: 1217432073@qq.com" ]
  [ "张印辉（通讯作者）男，1977年9月出生于河北省衡水市.现为昆明理工大学机电工程学院教授、博士生导师.主要研究方向为图像处理、机器视觉和机器智能. Email: yinhui_z@163.com" ]
- 基金信息：
  
  国家自然科学基金(62171206;62061022;61761024)
- DOI：10.12263/DZXB.20220085
  中图分类号： TP391;
- 收稿：2022-01-17，
  
  修回：2022-04-10，
  
  纸质出版：2023-04-25
- 稿件说明：
移动端阅览
何自芬,史本杰,张印辉等.多注意力融合的环高原湖泊遥感影像分割[J].电子学报,2023,51(04):885-895.

HE Zi-fen,SHI Ben-jie,ZHANG Yin-hui,et al.Remote Sensing Image Segmentation of Around Plateau Lakes Based on Multi-Attention Fusion[J].ACTA ELECTRONICA SINICA,2023,51(04):885-895.
何自芬,史本杰,张印辉等.多注意力融合的环高原湖泊遥感影像分割[J].电子学报,2023,51(04):885-895. DOI： 10.12263/DZXB.20220085.

HE Zi-fen,SHI Ben-jie,ZHANG Yin-hui,et al.Remote Sensing Image Segmentation of Around Plateau Lakes Based on Multi-Attention Fusion[J].ACTA ELECTRONICA SINICA,2023,51(04):885-895. DOI： 10.12263/DZXB.20220085.

摘要

环高原湖泊区域土地类别监测为湖泊生态保护和土地资源规划提供了决策依据.针对此区域遥感影像中河流、建筑物及植被目标分布零散、尺度不均导致分割精度较低的问题，设计了融合类别与多尺度注意力的遥感语义分割网络.该网络采用编码-解码的端到端结构并以深度残差神经网络为基础构建类别与多尺度注意力模块.类别注意力对网络特征层进行初步分类与空间信息过滤，有利于网络关注类别信息以降低像素分类误差；多尺度注意力将混合域注意力和多尺度特征进行融合，为不同尺度特征建立上下文联系，改善分布零散小尺度目标固有的分割消弥问题.实验结果表明，在建立的环滇池区域遥感影像语义分割数据集上，本文设计的注意力融合语义分割网络测试精度在平均交并比和平均像素精度指标下分别达到77.4%和86.3%.从整体分割效果来看，融合类别与多尺度注意力分割网络在一定程度上解决了分布零散小尺度目标区域的分割消弥问题，对环高原湖泊区域精准监测和科学规划提供了有效依据.

Abstract

Land category monitoring in lake region around plateau provides decision-making basis for lake ecological protection and land resource planning. Aiming at the problem of low segmentation accuracy caused by scattered distribution and uneven scale of rivers

buildings and vegetation in remote sensing images of this region

a remote sensing semantic segmentation network integrating category and multi-scale attention is designed. The network adopts encoding-decoding end-to-end structure and constructs class and multi-scale attention modules based on depth residual neural network. Category attention makes a preliminary classification and spatial information filtering for the network feature layer

which is beneficial for the network to pay attention to category information and reduce pixel classification error; multi-scale attention combines mixed domain attention with multi-scale features

establishes context connection for different scale features

and improves the inherent segmentation and elimination problem of scattered small-scale targets. Experiments are performed on the semantic segmentation data set of remote sensing images around Dianchi Lake

and the test accuracy of the attention fusion semantic segmentation network designed in this paper reaches 77.4% and 86.3% under the average intersection ratio and average pixel accuracy index

respectively. From the overall segmentation effect

the fusion category and multi-scale attention segmentation network solve the segmentation and elimination problem of scattered small-scale target areas to a certain extent

and provide an effective basis for accurate monitoring and scientific planning of lakes around plateau.

关键词

Keywords

references

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