Multi⁃Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification  

作　　者：Shanshan Zheng Wen Liu Rui Shan Jingyi Zhao Guoqian Jiang Zhi Zhang 

机构地区：[1]School of Science,Yanshan University,Qinhuangdao 066004,Hebei,China [2]College of Mechanical Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China [3]School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China [4]Beijing Institute of Space Mechanic&Electricity,Beijing 100094,China

出　　处：《Journal of Harbin Institute of Technology(New Series)》2021年第4期25-32,共8页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the Project of Multi Modal Monitoring Information Learning Fusion and Health Warning Diagnosis of Wind Power Transmission System(Grant No.61803329);the Research on Product Quality Inspection Method Based on Time Series Analysis(Grant No.201703A020);the Research on the Theory and Reliability of Group Coordinated Control of Hydraulic System for Large Engineering Transportation Vehicles(Grant No.51675461).

摘　　要：Aiming at the problem of image information loss,dilated convolution is introduced and a novel multi⁃scale dilated convolutional neural network(MDCNN)is proposed.Dilated convolution can polymerize image multi⁃scale information without reducing the resolution.The first layer of the network used spectral convolutional step to reduce dimensionality.Then the multi⁃scale aggregation extracted multi⁃scale features through applying dilated convolution and shortcut connection.The extracted features which represent properties of data were fed through Softmax to predict the samples.MDCNN achieved the overall accuracy of 99.58% and 99.92% on two public datasets,Indian Pines and Pavia University.Compared with four other existing models,the results illustrate that MDCNN can extract better discriminative features and achieve higher classification performance.

关 键 词：multi⁃scale aggregation dilated convolution hyperspectral image classification(HSIC) shortcut connection 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]