Application of bilateral fusion model based on CNN in hyperspectral image classification

被引：0
作者：
Gao H. [1 ]
Cao X. [1 ]
Yang Y. [1 ]
Hua Z. [1 ]
Li C. [1 ]
机构：
[1] College of Computer and Information Engineering, Hohai University, Nanjing
来源：
Tongxin Xuebao/Journal on Communications | 2020年 / 41卷 / 11期
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
Convolutional neural network; Hyperlink; Hyperspectral images classification; Transpose-convolution; Upsampling;
D O I：
10.11959/j.issn.1000-436x.2020238
中图分类号：
学科分类号：
摘要：
Aiming at the issues of decreasing spatial resolution and feature loss caused by pooling operation in depth CNN-based hyperspectral image classification algorithm, a bilateral fusion block network (DFBN)composed of bilateral fusion blocks was designed. The upper structure of bilateral fusion block was constituted by 1×1 convolution and hyperlink, which was used to transfer local spatial characteristics. The lower structure was constituted by pooling layer, convolutional layer, deconvolution layer and upsampling to enhance the characteristics of efficient discrimination. Experimental results on three benchmark hyperspectral image data sets illustrate that the model is superior to other similar classification models. © 2020, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：132 / 140
页数：8
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