摘要
遥感影像分类对于遥感数据分析尤显重要,是测绘学科中不可或缺的教学实践内容。传统分类方法的应用,在面对复杂和高维遥感影像数据时存在处理效率低和精度不高的问题。随着机器学习技术的快速发展,随机森林(Random Forest,RF)算法因分类精度较高和抗噪能力较强,成为了遥感影像分类领域的应用热点。文章提出了一种基于随机森林算法的遥感影像分类教学方法,构建了教学准备、学生实验及教学反馈的层次教学框架,分析了在遥感影像分类中此教学方法的具体应用及对学生测绘能力的提升影响。并且,通过实验验证了此教学方法可以有效地提升学生的遥感影像数据处理能力和机器学习应用能力,为测绘教学提供了新的思路和实践参考。
Remote sensing image classification is an indispensable step in remote sensing data analysis and is particularly important for surveying and mapping education.Although traditional classification methods have certain applications,they suffer from issues such as low processing efficiency and limited accuracy when faced with complex and high-dimensional remote sensing image data.With the rapid development of machine learning technology,the Random Forest(RF)algorithm has become a hot topic in remote sensing image classification due to its high classification accuracy and strong noise resistance.This paper proposes a remote sensing image classification teaching method based on the Random Forest algorithm,detailing the teaching design of three major stages:teaching preparation,student experiments,and teaching feedback.It analyzes the practical application of this method in remote sensing image classification and its impact on improving students’surveying and mapping abilities.Through experimental verification,the Random Forest-based teaching method effectively enhances students’remote sensing image data processing skills and machine learning application abilities,providing new ideas and practical guidance for surveying and mapping education.
作者
池深深
安德先
王磊
CHI Shenshen;AN Dexian;WANG Lei(School of Spatial Information and Geomatics,Anhui University of Science&Technology,Huainan 232001,China)
出处
《湖北文理学院学报》
2026年第2期5-11,共7页
Journal of Hubei University of Arts and Science
基金
安徽理工大学教育教学改革研究项目(2023xjxm021)
安徽理工大学课程思政建设研究项目(xjkcszjy2025218)。
关键词
测绘教学
遥感影像
随机森林算法
surveying and mapping education
remote sensing images
Random Forest algorithm
