计算机与数字工程2023，Vol.51Issue(11)：2490-2492,3.DOI:10.3969/j.issn.1672-9722.2023.11.003
基于深度置信网络的地表分类算法
Surface Classification Algorithm Based on Depth Belief Network
摘要
Abstract
In complex terrain environment,the data feature dimension is usually large and the data is not balanced.The tradi-tional shallow algorithms such as Softmax and Support Vector Machine(SVM)used in terrain recognition research decrease the rep-resentation ability and the classification accuracy is not ideal when facing complex terrain.In this paper,after studying the tradition-al methods and deep learning theory,Deep Belief Network(DBN)and Softmax are used for effective combination of terrain recogni-tion research,using the centrosymmetric local binary mode and color histogram to obtain features.Experimental results show that the proposed algorithm has better classification effect than the traditional algorithm.关键词
深度信念网络/Softmax/支持向量机/中心对称局部二值模式Key words
DBN/Softmax/SVM/CSLBP分类
信息技术与安全科学引用本文复制引用
张哲,郭剑辉,楼根铨,张文俊..基于深度置信网络的地表分类算法[J].计算机与数字工程,2023,51(11):2490-2492,3.基金项目
新疆建设兵团重点领域科技攻关项目(编号:2019BC010) （编号:2019BC010）
国家自然科学基金项目(编号:61603190)资助. （编号:61603190）
