大连理工大学学报2018，Vol.58Issue(2)：166-173,8.DOI:10.7511/dllgxb201802009
基于深度极限学习机的高光谱遥感影像分类研究
Hyperspectral remote sensing image classification based on deep extreme learning machine
摘要
Abstract
Hyperspectral remote sensing data are more and more popular and widely used.The accurate classification of surface obj ects based on hyperspectral remote sensing data is one of the core applications of hyperspectral remote sensing technology. For the classification problem of hyperspectral remote sensing image,a classification method is proposed based on deep extreme learning machine (D-ELM).This method uses a new depth learning model,that is D-ELM to classify hyperspectral remote sensing images.It is compared with extreme learning machine(ELM),support vector machine (SVM),kernal extreme learning machine(ELMK)classification methods.The results show that D-ELM classification method can excavate the spatial distribution regularity of the hyperspectral remote sensing image more accurately,and improve the accuracy of classification with respect to ELM,SVM and ELMK classification methods.关键词
高光谱遥感影像/深度学习/极限学习机/遥感影像分类Key words
hyperspectral remote sensing image/deep learning/extreme learning machine/remote sensing image classification分类
信息技术与安全科学引用本文复制引用
吕飞,韩敏..基于深度极限学习机的高光谱遥感影像分类研究[J].大连理工大学学报,2018,58(2):166-173,8.基金项目
国家自然科学基金资助项目(61374154) （61374154）
国家自然科学基金委科学仪器基础研究专项资助项目(51327004). （51327004）
