联合空-谱信息的高光谱影像深度三维卷积网络分类

doi:10.11947/j.AGCS.2019.20170578

测绘学报 ›› 2019, Vol. 48 ›› Issue (1): 53-63.doi: 10.11947/j.AGCS.2019.20170578

联合空-谱信息的高光谱影像深度三维卷积网络分类

刘冰, 余旭初, 张鹏强, 谭熊

信息工程大学, 河南郑州 450001

收稿日期:2017-10-27 修回日期:2018-09-10 出版日期:2019-01-20 发布日期:2019-01-31
作者简介:刘冰(1991-),男,博士生,研究方向为机器学习与高光谱影像分类。E-mail:liubing220524@126.com
基金资助:
河南省科技攻关计划（152102210014）

Deep 3D convolutional network combined with spatial-spectral features for hyperspectral image classification

LIU Bing, YU Xuchu, ZHANG Pengqiang, TAN Xiong

Information Engineering University, Zhengzhou 450001, China

Received:2017-10-27 Revised:2018-09-10 Online:2019-01-20 Published:2019-01-31
Supported by:
Key Scientific and Technological Project of Henan Province(No. 152102210014)

摘要/Abstract

摘要：

针对高光谱影像分类高维和小样本的特点，提出一种基于深度三维卷积神经网络的高光谱影像分类方法。首先，该方法直接以高光谱数据立方体为输入，利用三维卷积操作提取高光谱数据立方体的三维空-谱特征。然后，利用残差学习构建深层网络，提取更高层次的特征表达，以提高分类精度。最后，采用Dropout正则化方法防止过拟合。利用Pavia大学、Indian Pines和Salinas 3组高光谱数据进行试验验证，结果表明，与支持向量机和现有的基于深度学习的高光谱影像分类方法相比，该方法能有效提高高光谱影像的地物分类精度。

关键词: 高光谱影像分类, 卷积神经网络, 三维卷积, 残差学习

Abstract:

A classification method of hyperspectral images based on deep 3D convolution networks is proposed in order to deal with the high dimensional and small samples of hyperspectral image classification. The method first uses hyperspectral data cube as input, and uses 3D convolution operation to extract 3D spatial-spectral features of hyperspectral data cube. Then, the residual learning is used to construct the deep network and extract higher level feature expression to improve the classification accuracy. Finally, the Dropout regularization method is used to prevent overfitting. Experiments were conducted on the University of Pavia, Indian Pines and Salinas datasets, and the results demonstrate that compared with support vector machine and the existing deep learning classification method for hyperspectral images, the method can effectively improve the classification accuracy of hyperspectral image.

Key words: hyperspectral image classification, convolutional neural network, 3D convolution, residual learning

中图分类号:

P237

刘冰, 余旭初, 张鹏强, 谭熊. 联合空-谱信息的高光谱影像深度三维卷积网络分类[J]. 测绘学报, 2019, 48(1): 53-63.

LIU Bing, YU Xuchu, ZHANG Pengqiang, TAN Xiong. Deep 3D convolutional network combined with spatial-spectral features for hyperspectral image classification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1): 53-63.

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联合空-谱信息的高光谱影像深度三维卷积网络分类

Deep 3D convolutional network combined with spatial-spectral features for hyperspectral image classification

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