A high-precision image classification network model based on a voting mechanism  

作　　者：Jianghong Zhao Xin Wang Xintong Dou Yingxue Zhao Zexin Fu Ming Guo Ruiju Zhang 

机构地区：[1]School of Geomatics and Urban Spatial Informatics,Beijing University of Civil Engineering and Architecture,Beijing,People’s Republic of China [2]Key Laboratory for Urban Spatial Information of the Ministry of Natural Resources,Beijing,People’s Republic of China [3]Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring,Beijing,People’s Republic of China [4]Guangzhou Panyu Polytechnic,Guangzhou,People’s Republic of China

出　　处：《International Journal of Digital Earth》2022年第1期2168-2183,共16页国际数字地球学报（英文）

基　　金：supported by the Project of the Natural Science Foundation of Beijing[8172016];National Natural Science Foundation Project[41601409,41971350];Open Fund Project of State Key Laboratory of Surveying and Remote Sensing Information Engineering of Wuhan University[19E01];Open Fund Project of State Key Laboratory of Geographic Information Engineering[SKLGIE2019-Z-3-1];Special fund project for basic scientific research business expenses of municipal colleges and universities of Beijing Jianzhu University[X18063];National Key R&D Program Project[2018YFC0807806];Digital Mapping and Open Research Foundation Project of the Key Laboratory for Land Information Applications of the Ministry of Natural Resources[ZRZYBWD202102];the Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China(R20200287);Major Decision Consulting Project of the Beijing Social Science Foundation(21JCA004)。

摘　　要：With the development of satellite remote sensing technology,image classification task,as the basis of remote sensing data interpretation,has received wide attention to improving accuracy and robustness.At the same time,in-depth learning technology has been widely used in remote sensing and has a far-reaching impact.Since the existing image classification methods ignore the feature that the general image semantics are the same as the semantics of a single pixel,this paper presents an algorithm that uses the semantics of an image to achieve high-precision image classification.Based on the idea of partial substitution for global,this algorithm designs a split result voting mechanism and builds a Vgg-Vote network model.This mechanism votes on the semantically segmented result of an image and uses the maximum filtering function to select the category containing the most significant number of pixels as the prediction category of the image.Experiments on UC Merced Land-User complete datasets and five types of incomplete datasets with varying degrees of interference,including noise,data occlusion and loss,show that the Vote mechanism dramatically improves the classification accuracy,robustness and anti-jamming capability of Vgg-Vote.

关 键 词：Deep learning Image classification ROBUSTNESS Remote sensing image Vote mechanism 

分 类 号：TP7[自动化与计算机技术—检测技术与自动化装置]