[1]李亚飞,董红斌.基于卷积神经网络的遥感图像分类研究[J].智能系统学报,2018,13(4):550-556.[doi:10.11992/tis.201706078]
LI Yafei,DONG Hongbin.Classification of remote-sensing image based on convolutional neural network[J].CAAI Transactions on Intelligent Systems,2018,13(4):550-556.[doi:10.11992/tis.201706078]
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LI Yafei,DONG Hongbin.Classification of remote-sensing image based on convolutional neural network[J].CAAI Transactions on Intelligent Systems,2018,13(4):550-556.[doi:10.11992/tis.201706078]
基于卷积神经网络的遥感图像分类研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第4期
页码:
550-556
栏目:
学术论文—机器学习
出版日期:
2018-07-05
- Title:
- Classification of remote-sensing image based on convolutional neural network
- Keywords:
- remote-sensing image; classification of land cover; convolutional neural networks; feature fusion
- 分类号:
- TP301
- 摘要:
- 遥感图像分类是模式识别技术在遥感领域的具体应用，针对遥感图像处理中的分类问题，提出了一种基于卷积神经网络(convolutional neural networks，CNN)的遥感图像分类方法，并针对单源特征无法提供有效信息的问题，设计了一种多源多特征融合的方法，将遥感图像的光谱特征、纹理特征、空间结构特征等按空间维度以向量或矩阵的形式进行有效融合，以此训练CNN模型。实验表明，多源多特征相融合能够加快模型收敛速度，有效提高遥感图像的分类精度；与其他分类方法相比，CNN能够取得更高的分类精度，获得更优的分类效果。
- Abstract:
- The classification of remote-sensing images is a specific application of pattern recognition technology in the remote-sensing domain. In this paper, we propose a method for the classification of remote-sensing images based on convolutional neural networks (CNN). In addition, to address the difficulty of providing effective information regarding a single-source feature in convolutional neural networks, we propose a multi-source and multi-feature fusion method. We combine the spectral, texture, and spatial-structure features of remote-sensing images in the form of vectors or matrices according to their spatial dimensions, and train the CNN model using these combined features. The experimental results show that multi-source and multi-feature fusion can effectively improve the model convergence speed and classification accuracy, in comparison with traditional classification methods, and that the CNN method achieves higher classification accuracy and classification effect.
- 参考文献/References:
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备注/Memo
收稿日期:2017-06-26。
基金项目:国家自然科学基金项目（61472095）.
作者简介:李亚飞,男,1992年生,硕士研究生,主要研究方向为深度学习;董红斌,男,1963年生,教授,博士生导师,主要研究方向计算智能、机器学习、数据挖掘和多Agent系统。
通讯作者:董红斌.E-mail:donghongbin@hrbeu.edu.cn.
更新日期/Last Update:
2018-08-25