城市地质2024，Vol.19Issue(4)：490-499,10.DOI:10.3969/j.issn.2097-3764.2024.04.009
基于Sentinel-2遥感影像的自然资源分类提取研究
Research on natural resource classification and extraction based on Sentinel-2 remote sensing images
摘要
Abstract
One of the main means of the National Natural Resources Update Survey is to extract the surface cover through remote sensing images,and then to grasp the spatial distribution of various land types.Whether the satellite data source has a high revisit period is one of the important considerations for the selection of data sources of this work.This paper takes Zhangzhu Town of Yixing City as the study area,utilizes the high revisit advantage of Sentinel-2 satellite,and constructs a decision tree classification model by calculating the time-series NDVI values of different feature types and the spectral difference characteristics of different bands on the Sentinel-2 image.This model contains seven node layers,and determines the thresholds selected by different decision nodes according to the differences in spectral characteristics of the classified objects,and finally successfully classifies and extracts trees and shrubs.The thresholds selected by different decision nodes are determined according to the differences in spectral characteristics between the classified objects.The overall classification accuracy reaches 88.26%.By comparing with the extraction results relying only on the spectral characteristics of the features,it can be seen that the accuracy of the method of constructing a decision tree by introducing time series data is significantly improved,which proves that this method of extracting and classifying feature information based on the idea of time series data and decision tree of Sentinel-2 is of great practicability,and it can provide a methodological reference for the future investigation of the change of natural resources.关键词
哨兵2号/时序数据/光谱特性/地物分类/决策树Key words
Sentinel-2/time-series data/spectrum feature/objects classification/decision tree引用本文复制引用
刘知,刘小松,杨波,王鑫,张航..基于Sentinel-2遥感影像的自然资源分类提取研究[J].城市地质,2024,19(4):490-499,10.基金项目
山西省地质勘查建设与发展基金项目(2023-007)、山西省地质灾害防治重大专项项目(晋分采[2020-00162]G153-C53)联合资助 （2023-007）
