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基于集成卷积神经网络的遥感影像场景分类

Scene Classification of Remote Sensing Images Based on Integrated Convolutional Neural Networks

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摘要

提出了一种基于集成卷积神经网络(CNN)的遥感影像场景分类算法。通过构建反向传播网络实现了场景图像的复杂度度量; 根据图像的复杂度级别, 选择CNN对图像进行分类, 完成了遥感影像的场景分类。使用所提出的算法对NWPU-RESISC45公开数据集进行了实验验证, 取得了89.33%(第一类实验)和92.53%(第二类实验)的分类准确率, 平均运行时间为0.41 s。相比于精调训练的VGG-16模型, 所提算法的分类准确率分别提升了2.19%和2.17%, 预测速率提升了33%, 证明了其有效性和实用性。

Abstract

A scene classification algorithm of remote sensing images based on the integrated convolutional neural network (CNN) is proposed. A back-propagation network is constructed to measure the complexity of scene images. The classification of these images is conducted with the CNN based on the complexity level of each image, thus, the scene classification of remoting sensing images is achieved. With the proposed algorithm, the experimental verification of the open data of NWPU-RESISC45 is conducted and the classification accuracy of 89.33% for Type I test and that of 92.53% for Type II are obtained, respectively. The average running time is 0.41 s. Compared with the VGG-16 model for fine tuning and training, the classification accuracy by the proposed algorithm is increased by 2.19% and 2.17%, respectively. Simultaneously, the prediction rate is increased by 33%. Thus, the efficiency and practicality of this proposed algorithm are confirmed.

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中图分类号:TP753

DOI:10.3788/AOS201838.1128001

所属栏目:遥感与传感器

基金项目:国家自然科学基金青年基金(61505203)

收稿日期:2018-04-02

修改稿日期:2018-06-01

网络出版日期:2018-06-13

作者单位    点击查看

张晓男:中国科学院长春光学精密机械与物理研究所, 吉林 长春 130033中国科学院大学, 北京 100049
钟兴:中国科学院长春光学精密机械与物理研究所, 吉林 长春 130033长光卫星技术有限公司吉林省卫星遥感应用技术重点实验室, 吉林 长春 130102
朱瑞飞:中国科学院长春光学精密机械与物理研究所, 吉林 长春 130033长光卫星技术有限公司吉林省卫星遥感应用技术重点实验室, 吉林 长春 130102
高放:长光卫星技术有限公司吉林省卫星遥感应用技术重点实验室, 吉林 长春 130102
张作省:中国科学院长春光学精密机械与物理研究所, 吉林 长春 130033中国科学院大学, 北京 100049
鲍松泽:中国科学院长春光学精密机械与物理研究所, 吉林 长春 130033中国科学院大学, 北京 100049
李竺强:长光卫星技术有限公司吉林省卫星遥感应用技术重点实验室, 吉林 长春 130102

联系人作者:钟兴(ciomper@163.com); 张晓男(zhangxiaonan_93@163.com);

【1】Li E Z, Xia J S, Du P J, et al. Integrating multilayer features of convolutional neural networks for remote sensing scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10): 5653-5665.

【2】Cheng G, Han J W, Lu X Q. Remote sensing image scene classification: Benchmark and state of the art[J]. Proceedings of the IEEE, 2017, 105(10): 1865-1883.

【3】Zou Q, Ni L H, Zhang T, et al. Deep learning based feature selection for remote sensing scene classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(11): 2321-2325.

【4】Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1778-1790.

【5】Hu F, Xia G S, Hu J W, et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery[J]. Remote Sensing, 2015, 7(11): 14680-14707.

【6】Stumpf A, Kerle N. Object-oriented mapping of landslides using random forests[J]. Remote Sensing of Environment, 2011, 115(10): 2564-2577.

【7】Wang Y B, Zhang L Q, Tong X H, et al. A three-layered graph-based learning approach for remote sensing image retrieval[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6020-6034.

【8】Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]. International Conference on Advances in Neural Information Processing Systems, 2012: 1097-1105.

【9】Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2018-03-15]. https: ∥arxiv.org/pdf/1409.1556v6.pdf.

【10】Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.

【11】He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.

【12】Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700-4708.

【13】Yu Y L, Liu F X. A two-stream deep fusion framework for high-resolution aerial scene classification[J]. Computational Intelligence and Neuroscience, 2018, 2018: 8639367.

【14】Li H F, Peng J, Tao C, et al. What do we learn by semantic scene understanding for remote sensing imagery in CNN framework [EB/OL]. (2017-05-19)[2018-03-16]. https: ∥arxiv.org/ftp/arxiv/papers/1705/1705.07077.pdf

【15】Peters R A, Strickland R N. Image complexity metrics for automatic target recognizers[C]. Automatic Target Recognizer System and Technology Conference, 1990: 1-17.

【16】Rigau J, Feixas M, Sbert M. An information-theoretic framework for image complexity[C]. Computational Aesthetics 2005: Eurographics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging, 2005: 177-184.

【17】Cardaci M, Di Gesù V, Petrou M, et al. On the evaluation of images complexity: A fuzzy approach[C]. International Workshop on Fuzzy Logic and Applications, 2005: 305-311.

【18】Song Q H, Chen Z B, Sun S H, et al. A scene recognition method based on image complexity[J]. Proceedings of SPIE, 2014, 9282: 928221.

【19】Chen Y Q, Duan J, Zhu Y, et al. Research on the image complexity based on neural network[C]. Machine Learning and Cybernetics, 2015, 1: 295-300.

【20】Chen Y Q, Duan J, Zhu Y, et al. Research on the image complexity based on texture features[J]. Chinese Optics, 2015, 8(3): 407-414.
陈燕芹, 段锦, 祝勇, 等. 基于纹理特征的图像复杂度研究[J]. 中国光学, 2015, 8(3): 407-414.

【21】Stricker M A, Orengo M. Similarity of color images[J]. Proceedings of SPIE, 1995, 2420: 381-393.

【22】Gao C C, Hui X W. GLCM-based texture feature extraction[J]. Computer Systems and Applications, 2010, 19(6): 195-198.
高程程, 惠晓威. 基于灰度共生矩阵的纹理特征提取[J]. 计算机系统应用, 2010, 19(6): 195-198.

【23】Perez L, Wang J. The effectiveness of data augmentation in image classification using deep learning[EB/OL]. (2017-12-13)[2018-03-17]. https: ∥arxiv.org/pdf/1712.04621.pdf.

【24】Kingma D P, Ba J L. Adam: A method for stochastic optimization[EB/OL]. (2017-01-30)[2018-3-17]. https: ∥arxiv.org/pdf/1412.6980.pdf.

【25】Wang B B, Wang Y X. Some properties relating to stochastic gradient descent methods[J]. Journal of Mathematics, 2011, 31(6): 1041-1044.
汪宝彬, 汪玉霞. 随机梯度下降法的一些性质[J]. 数学杂志, 2011, 31(6): 1041-1044.

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