高分辨率遥感影像语义分割的半监督全卷积网络法

doi:10.11947/j.AGCS.2020.20190044

测绘学报 ›› 2020, Vol. 49 ›› Issue (4): 499-508.doi: 10.11947/j.AGCS.2020.20190044

高分辨率遥感影像语义分割的半监督全卷积网络法

耿艳磊^1,2, 陶超^1,2, 沈靖^1,2, 邹峥嵘^1,2

1. 中南大学地球科学与信息物理学院, 湖南长沙 410083;
2. 中南大学有色金属成矿预测与地质环境监测教育部重点实验室, 湖南长沙 410083

收稿日期:2019-01-24 修回日期:2019-07-11 发布日期:2020-04-17
通讯作者: 陶超 E-mail:kingtaochao@126.com
作者简介:耿艳磊(1993-),男,硕士,研究方向为高分辨率遥感影像智能解译。E-mail:gengyanlei@csu.edu.cn
基金资助:
国家自然科学基金（41771458）；国家重点研发项目（2018YFB0504501）；湖湘青年英才计划（2018RS3012）；湖南省国土厅国土资源科研项目（2017-13）；湖南省教育厅创新平台开放基金项目（18K005）

High-resolution remote sensing image semantic segmentation based on semi-supervised full convolution network method

GENG Yanlei^1,2, TAO Chao^1,2, SHEN Jing^1,2, ZOU Zhengrong^1,2

1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Ministry of Education, Changsha 410083, China

Received:2019-01-24 Revised:2019-07-11 Published:2020-04-17
Supported by:
The National Natural Science Foundation of China (No. 41771458);The National Key Research and Development Program (No. 2018YFB0504501);The Young Elite Scientists Sponsorship Program by Hunan province of China (No. 2018RS3012);Land and Resource Department Scientific Research Program of Hunan Province, China (No. 2017-13);Hunan Science and Technology Department Innovation Platform Open Fund Project (No. 18K005)

摘要/Abstract

摘要： 在遥感领域，利用大量的标签影像数据来监督训练全卷积网络，实现影像语义分割的方法会导致标签绘制成本昂贵，而少量标签数据的使用会导致网络性能下降。针对这一问题，本文提出了一种基于半监督全卷积网络的高分辨率遥感影像语义分割方法。通过采用一种集成预测技术，同时优化有标签样本上的标准监督分类损失及无标签数据上的非监督一致性损失，来训练端到端的语义分割网络。为验证方法的有效性，分别使用ISPRS提供的德国Vaihingen地区无人机影像数据集及国产高分一号卫星影像数据进行试验。试验结果表明，与传统方法相比，无标签数据的引入可有效提升语义分割网络的分类精度并可有效降低有标签数据过少对网络学习性能的影响。

关键词: 遥感影像, 语义分割, 半监督, 全卷积网络

Abstract: In the field of remote sensing, the method of realizing image semantic segmentation by using a large amount of label image data to supervise training full convolution network will result in expensive label drawing cost, while the use of a small amount of label data would lead to network performance degradation. To solve this problem, this paper proposes a semi-supervised full convolution network based semantic segmentation method for high resolution remote sensing images. Specifically, we explore an ensemble prediction technique to train the end-to-end semantic segmentation network by simultaneously optimizing a standard supervised classification loss on labeled samples along with an additional unsupervised consistence loss term imposed on labeled and unlabeled data. In the experiments, the image data set of Vaihingen in Germany provided by ISPRS and satellite GF-1 data were used, and the experimental results show that the proposed method can effectively improve the network performance degradation caused by using only a small amount of label data.

Key words: remote sensing image, semantic segmentation, semi-supervised, full convolution network

中图分类号:

P231

耿艳磊, 陶超, 沈靖, 邹峥嵘. 高分辨率遥感影像语义分割的半监督全卷积网络法[J]. 测绘学报, 2020, 49(4): 499-508.

GENG Yanlei, TAO Chao, SHEN Jing, ZOU Zhengrong. High-resolution remote sensing image semantic segmentation based on semi-supervised full convolution network method[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(4): 499-508.

参考文献

[1] 何小飞, 邹峥嵘, 陶超, 等. 联合显著性和多层卷积神经网络的高分影像场景分类[J]. 测绘学报, 2016, 45(9):1073-1080. DOI:10.11947/j.AGCS.2016.20150612. HE Xiaofei, ZOU Zhengrong, TAO Chao, et al. Combined saliency with multi-convolutional neural network for high resolution remote sensing scene classification[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(9):1073-1080. DOI:10.11947/j.AGCS.2016.20150612.
[2] ZUO Tongchun, FENG Juntao, CHEN Xuejin. HF-FCN:hierarchically fused fully convolutional network for robust building extraction[C]//Proceedings of the 13th Asian Conference on Computer Vision. Taipei, Taiwan, China:Springer, 2016.
[3] NORONHA S, NEVATIA R. Detection and modeling of buildings from multiple aerial images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(5):501-518.
[4] COTE M, SAEEDI P. Automatic rooftop extraction in nadir aerial imagery of suburban regions using corners and variational level set evolution[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1):313-328.
[5] LI Er, FEMIANI J, XU Shibiao, et al. Robust rooftop extraction from visible band images using higher order CRF[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(8):4483-4495.
[6] 胡翔云, 巩晓雅, 张觅. 变分法遥感影像人工地物自动检测[J]. 测绘学报, 2018, 47(6):780-789. DOI:10.11947/j.AGCS.2018.20170642. HU Xiangyun, GONG Xiaoya, ZHANG Mi. A variational approach for automatic man-made object detection from remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6):780-789. DOI:10.11947/j.AGCS.2018.20170642.
[7] 林祥国, 张继贤. 面向对象的形态学建筑物指数及其高分辨率遥感影像建筑物提取应用[J]. 测绘学报, 2017, 46(6):724-733. DOI:10.11947/j.AGCS.2017.20170068. LIN Xiangguo, ZHANG Jixian. Object-based morphological building index for building extraction from high resolution remote sensing imagery[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(6):724-733. DOI:10.11947/j.AGCS.2017.20170068.
[8] WANG Weixing, YANG Nan, ZHANG Yi, et al. A review of road extraction from remote sensing images[J]. Journal of Traffic and Transportation Engineering, 2016, 3(3):271-282.
[9] 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.
[10] 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:Curran Associates Inc., 2012.
[11] SAITO S, YAMASHITA T, AOKI Y. Multiple object extraction from aerial imagery with convolutional neural networks[J]. Journal of Imaging Science and Technology, 2016, 60(1):010402.
[12] ALSHEHHI R, MARPU P R, WOON W L, et al. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017(130):139-149.
[13] SAITO S, AOKI Y. Building and road detection from large aerial imagery[C]//Proceedings of SPIE 9405, Image Processing:Machine Vision Applications VIII. San Francisco, CA:SPIE, 2015:94050K.
[14] 范荣双, 陈洋, 徐启恒, 等. 基于深度学习的高分辨率遥感影像建筑物提取方法[J]. 测绘学报, 2019, 48(1):34-41. DOI:10.11947/j.AGCS.2019.20170638. FAN Rongshuang, CHEN Yang, XU Qiheng, et al. A high-resolution remote sensing image building extraction method based on deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1):34-41. DOI:10.11947/j.AGCS.2019.20170638.
[15] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA:IEEE, 2015:3431-3440.
[16] LAINE S, AILA T. Temporal Ensembling for semi-supervised learning[C]//Proceedings of 2017 International Conference on Learning Representations. Toulon, France:ICLR, 2017.
[17] TORRALBA A, FERGUS R, FREEMAN W T. 80 million tiny images:a large data set for nonparametric object and scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11):1958-1970.
[18] RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th Medical Image Computing and Computer-Assisted Intervention. Munich, Germany:Springer, 2015:234-241.
[19] CHEN L C, ZHU Yukun, PAPANDREOU G, et al. Encoder-decoder with Atrous separable convolution for semantic image segmentation[C]//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany:Springer, 2018:833-851.
[20] GATYS L A, ECKER A S, BETHGE M. Image style transfer using convolutional neural networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV:IEEE, 2016.
[21] IGLOVIKOV V, SHVETS A A. TernausNet:U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation. Computer Vision and Pattern Recognition, 2018.
[22] DENG Jia, DONG Wei, SOCHER R, et al. ImageNet:a large-scale hierarchical image database[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL:IEEE, 2009.
[23] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada:MIT Press, 2014.

高分辨率遥感影像语义分割的半监督全卷积网络法

High-resolution remote sensing image semantic segmentation based on semi-supervised full convolution network method

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