计算机与数字工程2024，Vol.52Issue(1)：206-212,7.DOI:10.3969/j.issn.1672-9722.2024.01.034
毛乌素沙漠植被的神经网络提取方法研究
Research on Extraction Method of Desert Vegetation Based on Neural Network
摘要
Abstract
As an important part of the human natural ecosystem,surface vegetation plays an important leading role in many as-pects,such as reducing soil erosion,maintaining ecological balance,improving windbreak and sand fixation capacity,and main-taining sustainable regional economic development.In recent years,satellite remote sensing technology has become a low-cost and high-efficiency vegetation cover estimation algorithm with its advantages of large-scale,multi-scale and multi-temporal phases,and has shown important application value in vegetation extraction and change monitoring.In this paper,the Mu Us Desert area of Yulin city is taken as the main research area,and the information of surface vegetation is extracted from landsat8 remote sensing im-ages by using the environment for visualizing images(ENVI)analysis software of mobile phones,and the vegetation information is classified and extracted by different supervised classification methods,and the quality accuracy of different supervised classification results is analyzed and evaluated.The results show that the neural network method has better performance than other methods in des-ert vegetation extraction,and can realize the dynamic change monitoring of desert vegetation.关键词
毛乌素沙漠/植被提取/神经网络/精度评定Key words
maowusu desert/vegetation extraction/neural network/precision evaluation分类
信息技术与安全科学引用本文复制引用
孟健,熊文豪,周寒,张晓倩,刘天琦,高贤君..毛乌素沙漠植被的神经网络提取方法研究[J].计算机与数字工程,2024,52(1):206-212,7.基金项目
长江大学2020年大学生创新创业训练计划(编号:Yz2020018)资助. （编号:Yz2020018）
