首页|期刊导航|哈尔滨工业大学学报（英文版）|Multi-Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification
哈尔滨工业大学学报（英文版）2021，Vol.28Issue(4)：25-32,8.
Multi-Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification
Multi?Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification
摘要
Abstract
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 connectionKey words
multi⁃scale aggregation/dilated convolution/hyperspectral image classification ( HSIC )/shortcut connection分类
信息技术与安全科学引用本文复制引用
Shanshan Zheng,Wen Liu,Rui Shan,Jingyi Zhao,Guoqian Jiang,Zhi Zhang..Multi-Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification[J].哈尔滨工业大学学报（英文版）,2021,28(4):25-32,8.基金项目
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),and the Research on the Theory and Reliability of Group Coordinated Control of Hydraulic System for Large Engineering Transportation Vehicles(Grant No.51675461). （Grant No.61803329）
