基于CNN的双边融合网络在高光谱图像分类中的应用  

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

作　　者：高红民[1] 曹雪莹 杨耀 花再军[1] 李臣明[1] GAO Hongmin;CAO Xueying;YANG Yao;HUA Zaijun;LI Chenming(College of Computer and Information Engineering,Hohai University,Nanjing 211100,China)

机构地区：[1]河海大学计算机与信息学院,江苏南京211100

出　　处：《通信学报》2020年第11期132-140,共9页Journal on Communications

基　　金：国家自然科学基金资助项目(No.61701166);中央高校基本科研业务费基金资助项目(No.B200202183);江苏省研究生科研与实践创新计划基金资助项目(No.SJCX20_0181);国家重点研发计划基金资助项目(No.2018YFC1508106)。

摘　　要：针对基于深度卷积神经网络的高光谱图像分类算法中存在的空间分辨率下降、池化操作引发特征丢失从而导致分类精度下降的问题,设计了一种由双边融合块构成的双边融合块网络。1×1卷积与超链接构成双边融合块上结构,传递局部空间特征,池化、卷积、反卷积、上采样组成下结构,强化高效判别特征。在3个基准高光谱图像数据集上的实验结果表明,该模型优于其他同类分类模型。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.

关 键 词：卷积神经网络 高光谱图像分类 转置卷积 上采样 超链接 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]