摘要
随着遥感技术和计算机技术的不断发展,传统的遥感影像分类方法已不能满足如今遥感影像分类的需求。近年来,随着深度学习方面研究成果的不断涌现,它给遥感影像的分类提供了一种新的思路和方法。首先概述了遥感影像分类的发展和深度学习的基本概念,然后重点介绍了基于深度置信网、卷积神经网络和栈式自动编码器等深度学习模型在遥感影像分类中的研究进展,最后提出了目前研究中存在的问题及遥感影像分类的发展趋势。
With the continuous development of remote sensing technology and computer technology,the traditional remote sensing image classification method cannot meet the needs of remote sensing image classification. In recent years,with the continuous emergence of research results in deep learning,it provides a new way for the classification of remote sensing images.This paper first outlined the development of remote sensing image classification and the basic concepts of deep learning,and then focused on the research progress in the classification of remote sensing images based on the deep learning model such as deep belief network,convolutional neural network and stacked auto-encoder. Finally,it summarized the existing issues and the future directions of remote sensing image classification based on deep learning.
作者
付伟锋
邹维宝
Fu Weifeng;Zou Weibao(College of Geology Engineering ＆ Geomatics,Chang＇an University,Xi＇an 710054,China)
出处
《计算机应用研究》
CSCD
北大核心
2018年第12期3521-3525,共5页
Application Research of Computers
关键词
深度置信网
卷积神经网络
栈式自动编码器
遥感影像分类
深度学习
deep belief network
convolution neural network
stacked auto-encoder
remote sensing image classification
deep learning
