基于深度置信网络的地表分类算法  

Surface Classification Algorithm Based on Depth Belief Network

作　　者：张哲 郭剑辉[1] 楼根铨 张文俊 ZHANG Zhe;GUO Jianhui;LOU Genquan;ZHANG Wenjun(School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094;Jiangnan Shipbuilding(Group)Co.,Ltd.,Shanghai 201913)

机构地区：[1]南京理工大学计算机科学与工程学院,南京210094 [2]江南造船(集团)有限责任公司,上海201913

出　　处：《计算机与数字工程》2023年第11期2490-2492,共3页Computer & Digital Engineering

基　　金：新疆建设兵团重点领域科技攻关项目(编号:2019BC010);国家自然科学基金项目(编号:61603190)资助。

摘　　要：复杂地形环境下的数据特征维度往往较大以及数据不平衡,传统地形识别研究使用的浅层算法如Softmax、支持向量机(Support Vector Machine,SVM)等面对复杂地形时表征能力下降分类精度不理想。论文在研究了传统方法和深度学习理论后,使用深度置信网络(Deep Belief Network,DBN)与Softmax进行有效结合后应用于地形识别研究,利用中心对称局部二值模式与颜色直方图结合获得特征,实验证明论文提出的算法比传统算法展现了更好的分类效果。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 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]