Surface features extraction in remote sensing images based on architecture-variant CNN

doi:10.11947/j.AGCS.2019.20180122

Abstract

Abstract: To exceed limited capacity of established convolutional neural network(CNN) with fixed architecture in traditional surface feature extraction in remote sensing images, we propose a new feature extraction method based on architecture-variant convolutional neural network (AVCNN). In AVCNN, key units are variables and the performance of the unknown model become object function. That means architecture search is added before traditional weights solving. Genetic algorithm is introduced to search proper architecture and classical algorithm is used to solve unknown weights in the candidate CNN. The CNN with final architecture is used to extract the surface feature in remote sensing images. The experiment result shows that AVCNN has flexible capacity and performances well in surface features extraction in remote sensing images.

Key words: surface feature extraction, convolution neural network, variant architecture, genetic algorithm

CLC Number:

P237

WANG Huabin, HAN Min, WANG Guanghui, LI Yu. Surface features extraction in remote sensing images based on architecture-variant CNN[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(5): 583-596.

References

[1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe. Nevada:ACM, 2012:1097-1105.
[2] ZANG Liangpei, ZHANG Lefei, DU Bo. Deep learning for remote sensing data:a technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2):22-40.
[3] 闫利, 江维薇. 一种利用结构特征的高分辨率遥感影像种植园自动提取方法[J]. 测绘学报, 2016, 45(9):1065-1072. DOI:10.11947/j.AGCS.2016.20150511. YAN Li, JIANG Weiwei. A structure feature for automatic extraction of plantation from high-resolution remote sensing imagery[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(9):1065-1072. DOI:10.11947/j.AGCS.2016.20150511.
[4] 郭军士. 基于改进形态学指数的ZY3影像建筑物和阴影的提取及应用[D]. 西安:西安科技大学, 2015. GUO Junshi. Extraction and application of building and shadow of ZY3 image based on improving morphological index[D]. Xi'an:Xi'an University of Science and Technology, 2015.
[5] 张莹莹. 高分辨率遥感影像中典型道路提取方法研究[D]. 哈尔滨:哈尔滨工业大学, 2016. ZHANG Yingying. Research on methods of typical road extraction from high-resolution remote sensing image[D]. Harbin:Harbin Institute of Technology, 2016.
[6] 李航. 统计学习方法[M]. 北京:清华大学出版社, 2012. LI Hang. Statistical learning method[M]. Beijing:Tsinghua University Press, 2012.
[7] 许夙晖, 慕晓冬, 赵鹏, 等. 利用多尺度特征与深度网络对遥感影像进行场景分类[J]. 测绘学报, 2016, 45(7):834-840. DOI:10.11947/j.AGCS.2016.20150623. XU Suhui, MU Xiaodong, ZHAO Peng, et al. Scene classification of remote sensing image based on multi-scale feature and deep neural network[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(7):834-840. DOI:10.11947/j.AGCS.2016.20150623.
[8] KAMPFFMEYER M, SALBERG A B, JENSSEN R. Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Las Vegas, NV:IEEE, 2016:680-688.
[9] ZHANG Fan, DU Bo, ZHANG Liangpei, et al. Weakly supervised learning based on coupled convolutional neural networks for aircraft detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(9):5553-5563.
[10] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[11] HU Fan, XIA Guisong, HU Jingwen, 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.
[12] ZHANG Fan, DU Bo, ZHANG Liangpei. Saliency-guided unsupervised feature learning for scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4):2175-2184.
[13] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651.
[14] RONNEBERGER O, FISCHER P, BROX T. U-net:convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany:Springer, 2015:234-241.
[15] BADRINARAYANAN V, KENDALL A, CIPOLLA R. Segnet:a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12):2481-2495.
[16] IOFFE S, SZEGEDY C. Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning. Lille, France:[s.n.], 2015:448-456.
[17] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Columbus, OH:IEEE, 2014:215-228.
[18] GIRSHICK R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015.
[19] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[20] HE Kaiming, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 2017:2980-2988.
[21] CHEN Fen, REN Ruilong, VAN DE VOORDE T, et al. Fast automatic airport detection in remote sensing images using convolutional neural networks[J]. Remote Sensing, 2018, 10(3):443.
[22] HOLLAND J H. Adaptation in natural and artificial systems[M]. Cambridge, Massachusetts:MIT Press, 1992.
[23] 李明. 遗传算法的改进及其在优化问题中的应用研究[D]. 长春:吉林大学, 2004. LI Ming. The study on improved genetic algorithm and its application in optimization questions[D]. Changchun:Jilin University, 2004.
[24] CLEVERT D A C, UNTERTHINER T, HOCHREITER S. Fast and accurate deep network learning by exponential linear units (ELUs)[R]. International Conference on Learning Representations 2016. San Juan:ICLR, 2016.
[25] KINGMA D P, BA J. ADAM:a method for stochastic optimization[C]//Proceedings of International Conference on Learning Representations 2015. San Diego:ICLR, 2015:321-335.
[26] TAN P, STEINBACH M, KUMAR V. 数据挖掘导论[M]. 范明, 范宏建, 译. 北京:人民邮电出版社,2011. TAN P, STEINBACH M, KUMAR V. Introduction to data mining[M]. FAN Ming, FAN Hongjian, trans. Beijing:Post & Telecom Press, 2011.
[27] CHANG C C, LIN C J. LIBSVM:a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):No.27.
[28] MENDOZA H, KLEIN A, FEURER M, et al. Towards automatically-tuned neural networks[C]//Proceedings of JMLR:Workshop and Conference Proceedings.[S.l.]:JMLR, 2016:58-65.
[29] KANDASAMY K, NEISWANGER W, SCHNEIDER J, et al. Neural architecture search with Bayesian optimisation and optimal transport[C]//Proceedings of the 32nd Conference on Neural Information Processing Systems. Montréal, Canada:[s.n.], 2018.
[30] ASSUNÇO F, LOURENÇO N, MACHADO P, et al. DENSER:deep evolutionary network structured representation[J]. Genetic Programming and Evolvable Machines, 2019, 20(1):5-35.
[31] DUFOURQ E, BASSET B A. EDEN:evolutionary deep networks for efficient machine learning[C]//Proceedings of 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference. Bloemfontein, South Africa:[s.n.], 2017.