Surface Classification Algorithm Based on Depth Belief Network
ZHANG Zhe
GUO Jianhui
LOU Genquan
ZHANG Wenjun
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.
Keywords：DBNSoftmaxSVMCSLBP
Publication Date：2023-11-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：3( 2490-2492 )