[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(04):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(04):550-556.[doi:10.11992/tis.201706078]
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基于卷积神经网络的遥感图像分类研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年04期
页码:
550-556
栏目:
出版日期:
2018-07-05

文章信息/Info

Title:
Classification of remote-sensing image based on convolutional neural network
作者:
李亚飞 董红斌
哈尔滨工程大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001
Author(s):
LI Yafei DONG Hongbin
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
关键词:
遥感图像地物分类卷积神经网络特征融合
Keywords:
remote-sensing imageclassification of land coverconvolutional neural networksfeature fusion
分类号:
TP301
DOI:
10.11992/tis.201706078
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:

[1] ?刘大伟, 韩玲, 韩晓勇. 基于深度学习的高分辨率遥感影像分类研究[J]. 光学学报, 2016, 36(4):0428001. LIU Dawei, HAN Ling, HAN Xiaoyong. High spatial resolution remote sensing image classification based on deep learning[J]. Acta optica sinica, 2016, 36(4):0428001.
[2] ALBERGA V. A study of land cover classification using polarimetric SAR parameters[J]. International journal of remote sensing, 2007, 28(17):3851-3870.
[3] HAGNER O, REESE H. A method for calibrated maximum likelihood classification of forest types[J]. Remote sensing of environment, 2007, 110(4):438-444.
[4] NIU Xin, BAN Yifang. Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach[J]. International journal of remote sensing, 2013, 34(1):1-26.
[5] HEERMANN P D, KHAZENIE N. Classification of multispectral remote sensing data using a back-propagation neural network[J]. IEEE transactions on geoscience and remote sensing, 1992, 30(1):81-88.
[6] PAL M, MATHER P M. An assessment of the effectiveness of decision tree methods for land cover classification[J]. Remote sensing of environment, 2003, 86(4):554-565.
[7] BENGIO Y. Learning deep architectures for AI[J]. Foundations and trends in machine learning, 2009, 2(1):1-127.
[8] 杜培军, 夏俊士, 薛朝辉, 等. 高光谱遥感影像分类研究进展[J]. 遥感学报, 2016, 20(2):236-256, doi:10.11834/jrs.20165022. DU Peijun, XIA Junshi, XUE Zhaohui, et al. Review of hyperspectral remote sensing image classification[J]. Journal of remote sensing, 2016, 20(2):236-256.
[9] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[10] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7):1527-1554.
[11] SCHROFF F, KALENICHENKO D, PHILBIN J. Facenet:a unified embedding for face recognition and clustering[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA, 2015:815-823.
[12] ZHANG Yu, BAI Xiangzhi, WANG Tao. Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure[J]. Information fusion, 2017, 35:81-101.
[13] TATULLI E, HUEBER T. Feature extraction using multimodal convolutional neural networks for visual speech recognition[C]//Proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing. New Orleans, USA, 2017:2971-2975.
[14] 李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述[J]. 计算机应用, 2016, 36(9):2508-2515. LI Yandong, HAO Zongbo, LEI Hang. Survey of convolutional neural network[J]. Journal of computer applications, 2016, 36(9):2508-2515.
[15] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6):84-90.
[16] HARALICK R M, SHANMUGAM K, DINSTEIN I. Texture features for image classification[J]. IEEE transactions on systems, man and cybernetics, 1973, 3(6):610-621.
[17] 张策, 臧淑英, 金竺, 等. 基于支持向量机的扎龙湿地遥感分类研究[J]. 湿地科学, 2011, 9(3):263-269. ZHANG Ce, ZANG Shuying, JIN Zhu, et al. Remote sensing classification for Zhalong Wetlands based on support vector machine[J]. Wetland science, 2011, 9(3):263-269.
[18] YUAN H, VAN DER WIELE C F, KHORRAM S. An Automated artificial neural network system for land use/land cover classification from Landsat TM imagery[J]. Remote sensing, 2009, 1(3):243-265.
[19] NA Xiaodong, ZANG Shuying, LIU Lei, et al. Wetland mapping in the Zhalong National Natural Reserve, China, using optical and radar imagery and topographical data[J]. Journal of applied remote sensing, 2013, 7(1):073554.
[20] SHARMA A, LIU Xiuwen, YANG Xiaojun, et al. A patch-based convolutional neural network for remote sensing image classification[J]. Neural networks, 2017, 95:19-28.

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备注/Memo

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