基于深度卷积神经网络和多核学习的遥感图像分类方法

王鑫; 李可; 宁晨; 黄凤辰

doi:10.11999/JEIT180628

基于深度卷积神经网络和多核学习的遥感图像分类方法

doi: 10.11999/JEIT180628

王鑫^1, ,,
李可¹,
宁晨²,
黄凤辰¹

1.
河海大学计算机与信息学院南京 211100
2.
南京师范大学物理科学与技术学院南京 210000

基金项目: 教育部中央高校基本科研业务费专项资金(2019B15314)，国家自然科学基金(61603124)，江苏省“六大人才高峰”高层次人才项目(XYDXX-007)，江苏省“333高层次人才培养工程”，江苏政府留学奖学金项目

详细信息

作者简介:
王鑫：女，1981年生，副教授，研究方向为图像处理、模式识别、计算机视觉、机器学习

李可：女，1996年生，硕士生，研究方向为深度学习理论

宁晨：男，1978年生，讲师，研究方向为机器学习和模式识别

黄凤辰：男，1964年生，副教授，研究方向为图像处理和分析

通讯作者:
王鑫　wang_xin@hhu.edu.cn

中图分类号: TP751
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出版历程
- 收稿日期: 2018-06-27
- 修回日期: 2018-12-28
- 网络出版日期: 2019-01-03
- 刊出日期: 2019-05-01

Remote Sensing Image Classification Method Based on Deep Convolution Neural Network and Multi-kernel Learning

1.
College of Computer and Information, Hohai University, Nanjing 211100, China
2.
School of Physics and Technology, Nanjing Normal University, Nanjing 210000, China

Funds: Fundamental Research Funds for the Central Universities (2019B15314), The National Natural Science Foundation of China (61603124), Six Talents Peak Project of Jiangsu Province (XYDXX-007), 333 High-Level Talent Training Program of Jiangsu Province, Jiangsu Province Government Scholarship for Studying Abroad

摘要

摘要:
为解决传统遥感图像分类方法特征提取过程复杂、特征表现力不强等问题，该文提出一种基于深度卷积神经网络和多核学习的高分辨率遥感图像分类方法。首先基于深度卷积神经网络对遥感图像数据集进行训练，学习得到两个全连接层的输出将作为遥感图像的两种高层特征；然后采用多核学习理论训练适合这两种高层特征的核函数，并将它们映射到高维空间，实现两种高层特征在高维空间的自适应融合；最后在多核融合特征的基础上，设计一种基于多核学习-支持向量机的遥感图像分类器，对遥感图像进行精确分类。实验结果表明，与目前已有的基于深度学习的遥感图像分类方法相比，该算法在分类准确率、误分类率和Kappa系数等性能指标上均有所提升，在实验测试集上3个指标分别达到了96.43%, 3.57%和96.25%，取得了令人满意的结果。
- 高分辨率遥感图像 /
- 分类 /
- 卷积神经网络 /
- 多核学习
Abstract:
To solve the problems of complex feature extraction process and low characteristic expressiveness of traditional remote sensing image classification methods, a high resolution remote sensing image classification method based on deep convolution neural network and multi-kernel learning is proposed. Firstly, the deep convolution neural network is constructed to train the remote sensing image data set to learn the outputs of two fully connected layers, which are taken as two high-level features of remote sensing images. Then, the multi-kernel learning is used to train the kernel functions for these two high-level features, so that they can be mapped to the high dimensional space, where these two features are fused adaptively. Finally, with the combined features, a remote sensing image classifier based on Multi-Kernel Learning-Support Vector Machine (MKL-SVM) is designed for remote sensing image classification. Experimental results show that compared with the existing deep learning based remote sensing classification methods, the proposed algorithm achieves improved results in terms of classification accuracy, error, and Kappa coefficient. On the experimental test set, the above three indicators reach 96.43%, 3.57%, and 96.25% respectively, and satisfactory results are obtained.
- High resolution remote sensing image /
- Classification /
- Convolution neural network /
- Multi-Kernel Learning(MKL)

HTML全文

图 1 本文算法的框图

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图 2 搭建的7层卷积神经网络

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图 3 21类高分辨率遥感图像示例

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图 4 conv1中96个卷积核可视化结果

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图 5 各卷积层学习得到的特征图

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图 6 3种算法的分类混淆矩阵

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表 1 3种算法各分类性能指标值

指标算法1 算法2 本文算法

Ac 0.8808 0.9337 0.9643
Er 0.1192 0.0663 0.0357
Kappa系数 0.8748 0.9304 0.9625

下载: 导出CSV

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