基于深度学习SE_ResNet模型的皖江洪水调蓄区湿地类型遥感识别
作者:
作者单位:

安徽师范大学 地理与旅游学院

中图分类号:

X37;P237

基金项目:

国家科技基础资源调查专项(2023FY100101);安徽省中青年教师培养行动项目(YQZD2023009); 安徽省高等学校省级质量工程项目(2023jyxm0164); 安徽省自然科学基金项目(2408085MD097)


Remote sensing identification of wetland types in the flood control and storage area of Yangtze River (Anhui section) based on deep learning
Author:
Affiliation:

School of Geography and Tourism, Anhui Normal University

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    摘要:

    由于湿地类别多样且结构复杂,湿地遥感分类识别极具挑战。为了快速、准确地遥感识别湿地类型,本文以皖江洪水调蓄区为研究区,基于Sentinel-2影像获取的光谱特征、植被和水体指数特征以及纹理特征构建样本集,引入深度学习压缩-激发与残差网络(SE_ResNet)模型开展湿地类型遥感识别研究。结果显示,SE_ResNet模型湿地类型识别显著优于传统监督分类最大似然法和机器学习随机森林法,总体精度分别提高19.00个百分点和10.25个百分点,达到了94%,Kappa系数达到0.90。SE_ResNet模型可以精细识别出不同湿地类型,特别是河流湿地、洪泛平原湿地和淡水湖湿地,识别结果比全球30m湿地数据产品(GWL_FCS30)和湖泊型流域自然-人文综合数据集(CODCLAB)更为精细和准确。

    Abstract:

    Due to the diverse types and complex structures of wetlands, the classification and identification of wetlands by remote sensing is extremely challenging. In order to quickly and accurately identify wetland types by remote sensing, this paper takes the flood storage area of the Wanjiang River as the study area, constructs a sample set based on the spectral features, vegetation and water index features and texture features obtained from Sentinel-2 images, and introduces the deep learning compression-excitation and residual network (SE_ResNet) model to carry out the research on remote sensing identification of wetland types. The results show that the wetland type recognition of the SE_ResNet model is significantly better than the traditional supervised classification maximum likelihood method and the machine learning random forest method, and the overall accuracy is increased by 19.00 percentage points and 10.25 percentage points, respectively, to 94%, and the Kappa coefficient reaches 0.90. The SE_ResNet model can finely identify different wetland types, especially river wetlands, flood plain wetlands and freshwater lake wetlands, and the identification results are more refined and accurate than the global 30m wetland data product (GWL_FCS30) and the lake-type watershed natural-human integrated dataset (CODCLAB).

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于梦琴,季青,张志明,王伟,姚有如,林跃胜,刘娜娜.基于深度学习SE_ResNet模型的皖江洪水调蓄区湿地类型遥感识别[J].南京信息工程大学学报,,():

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历史
  • 收稿日期:2024-12-25
  • 最后修改日期:2025-02-21
  • 录用日期:2025-02-27

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