首页|基于特征优选和机器学习的第四系空间信息提取研究

基于特征优选和机器学习的第四系空间信息提取研究

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[目的]第四系土体是土质滑坡的主要物源，其分布及厚度是开展土质滑坡隐患识别的重要基础。随着机器学习技术的兴起，图像分类技术与人工智能算法结合已成为遥感识别的主流。[方法]以三峡库首秭归向斜盆地为研究区，以Landsat-8影像为基础数据源，以区内现有土质滑坡数据构建样本，采用机器学习软件EnMAP-Box，建立第四系厚度及空间分布信息的随机森林分类模型，筛选出用于识别第四系土体厚度的最优特征子集，得出第四系相对厚度空间分布。[结果]结果表明:Landsat-8遥感影像的光谱特征、主成分、植被指数、湿度、坡度、绿度、均值等与第四系厚度具有强相关性，可作为识别第四系土体厚度的重要特征因子;随机森林模型能有效识别第四系土体厚度信息，且对岩质区提取精度较高;经实地调查验证，模型性能均衡，预测结果合理，可用于多植被中低山区环境的第四系识别。[结论]研究成果可为土质滑坡隐患识别和风险防控提供重要数据支撑。
Quaternary spatial information extraction based on feature selection and machine learning
[Objective]Quaternary soil is the main source of soil landslide and its distribution and thickness are the important ba-sis for identifying the hidden danger of soil landslide.With the rise of machine learning,the combination of image classification and artificial intelligence algorithm has become the mainstream of remote sensing recognition.[Methods]In this paper,the Zigui syncline Basin of the Three Gorges Reservoir is taken as the research area,the Landsat-8 remote sensing image is used as the basic data source,the existing soil landslide data in the area is used to construct samples,and the machine learning software EnMAP-Box is used to establish the random forest classification model of the quaternary thickness and spatial distribution infor-mation,and screen out the optimal feature subset for identifying the Quaternary soil thickness.The relative thickness spatial distribution of Quaternary system is obtained.[Results]The result show that the spectral characteristics,principal components,vegetation index,humidity,slope,greenness and mean value of Landsat-8 remote sensing images are strongly correlated with the thickness of Quaternary soil,which can be used as an important characteristic factor to identify the thickness of Quaternary soil.The random forest model can effectively identify the soil thickness information of Quaternary system,and the extraction accuracy of rocky area is high.The result of field investigation show that the model has balanced performance and reasonable prediction result,which can be used to identify the Quaternary system in multi-vegetation middle-low mountain environment.[Conclusion]The research result can provide important data support for soil landslide hidden danger identification and risk prevention and control.

Quaternary soillandsliderelative thicknessmachine learningspatial information extractionhead area of the Three Gorges Reservoir

李清清、黄海峰、张瑞、易武、周红、邓志勇、董志鸿、柳青、易庆林

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三峡大学湖北长江三峡滑坡国家野外科学观测研究站,湖北宜昌 443002

三峡大学三峡库区地质灾害教育部重点实验室,湖北宜昌 443002

三峡大学湖北省水电工程智能视觉监测重点实验室,湖北宜昌 443002

宜昌市地质环境监测站,湖北宜昌 443099

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第四系土体 滑坡 相对厚度 机器学习 空间信息提取 三峡库首

国家自然科学基金国家自然科学基金国家自然科学基金三峡库区地质灾害教育部重点实验室开放基金水电工程智能视觉监测湖北省重点实验室开放基金

U21A203142007237421074892020KDZ092020SDSJ02

2024

10.13928/j.cnki.wrahe.2024.05.014

水利水电技术(中英文)
水利部发展研究中心

水利水电技术(中英文)

CSTPCD北大核心
影响因子：0.456
ISSN：1000-0860
年,卷(期)：2024.55(5)