[1]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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Classification of remote-sensing image based on convolutional neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 4 Page number: 550-556 Column: 学术论文—机器学习 Public date: 2018-07-05
Title:
Classification of remote-sensing image based on convolutional neural network
Author(s):
LI Yafei;  DONG Hongbin
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Keywords:
remote-sensing image; classification of land cover; convolutional neural networks; feature fusion
CLC:
TP301
DOI:
10.11992/tis.201706078
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.
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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