激光与光电子学进展, 2019, 56 (15): 152801, 网络出版: 2019-08-05   

基于DeepLab-v3+的遥感影像分类 下载: 1558次

Remote Sensing Image Classification Based on DeepLab-v3+
作者单位
1 北京科技大学自动化学院, 北京 100083
2 北京科技大学计算机与通信工程学院, 北京 100083
引用该论文

袁立, 袁吉收, 张德政. 基于DeepLab-v3+的遥感影像分类[J]. 激光与光电子学进展, 2019, 56(15): 152801.

Li Yuan, Jishou Yuan, Dezheng Zhang. Remote Sensing Image Classification Based on DeepLab-v3+[J]. Laser & Optoelectronics Progress, 2019, 56(15): 152801.

参考文献

[1] 李石华, 王金亮, 毕艳, 等. 遥感图像分类方法研究综述[J]. 国土资源遥感, 2005, 17(2): 1-6.

    Li S H, Wang J L, Bi Y, et al. A review of methods for classification of remote sensing images[J]. Remote Sensing for Land & Resources, 2005, 17(2): 1-6.

[2] 单宝华, 霍晓洋, 刘洋. 一种极线约束修正数字图像相关匹配的立体视觉测量方法[J]. 中国激光, 2017, 44(8): 0804003.

    Shan B H, Huo X Y, Liu Y. A stereovision measurement method using epipolar constraint to correct digital image correlation matching[J]. Chinese Journal of Lasers, 2017, 44(8): 0804003.

[3] 赵方珍, 梁海英, 巫湘林, 等. 基于局部和全局高斯拟合的主动轮廓分割模型[J]. 激光与光电子学进展, 2017, 54(5): 051006.

    Zhao F Z, Liang H Y, Wu X L, et al. Active contour segmentation model based on local and global Gaussian fitting[J]. Laser & Optoelectronics Progress, 2017, 54(5): 051006.

[4] 宋昱, 吴一全, 毕硕本. 边缘修正CV模型的卫星遥感云图分割方法[J]. 光学学报, 2014, 34(9): 0901004.

    Song Y, Wu Y Q, Bi S B. Satellite remote sensing cloud image segmentation using edge corrected CV model[J]. Acta Optica Sinica, 2014, 34(9): 0901004.

[5] Cheriyadat A M. Unsupervised feature learning for aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 439-451.

[6] 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 D W, Han L, Han X Y. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.

[7] Alberga V. A study of land cover classification using polarimetric SAR parameters[J]. International Journal of Remote Sensing, 2007, 28(17): 3851-3870.

[8] Hagner O, Reese H. A method for calibrated maximum likelihood classification of forest types[J]. Remote Sensing of Environment, 2007, 110(4): 438-444.

[9] Niu X, Ban Y F. 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.

[10] 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.

[11] 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.

[12] BengioY. Learning deep architectures for AI[M]. Foundations and Trends® in Machine Learning, 2009, 2( 1): 1- 127.

[13] 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.

    Du P J, Xia J S, Xue Z H, et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 2016, 20(2): 236-256.

[14] 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.

[15] RonnebergerO, FischerP, BroxT. U-Net: convolutional networks for biomedical image segmentation[M] ∥Navab N, Hornegger J, Wells W, et al. Medical image computing and computer-assisted intervention. Lecture notes in computer science. Cham: Springer, 2015, 9351: 234- 241.

[16] Chen LC, GeorgeP, IasonasK, et al. Semantic image segmentation with deep convolutional nets and fully connectedCRFs[J/OL]. ( 2016-06-07)[2018-12-01]. https:∥arxiv.org/abs/1412. 7062.

[17] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.

[18] Chen LC, PapandreouG, SchroffF, et al. Rethinking atrous convolution for semantic image segmentation[J/OL]. ( 2017-12-05)[2018-12-01]. https:∥arxiv.org/abs/1706. 05587.

[19] 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.

[20] Chen LC, Zhu YK, PapandreouG, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M] ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 2018, 11211: 833- 851.

[21] GraumanK, DarrellT. The Pyramid match kernel: discriminative classification with sets of image features[C]∥Tenth IEEE International Conference on Computer Vision (ICCV'05), October 17-21, 2005, Beijing, China. New York: IEEE, 2005: 8824338.

[22] LazebnikS, SchmidC, PonceJ. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories[C]∥2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-( CVPR'06), June 17-22, 2006, New York, NY, USA. New York: IEEE, 2006.

[23] He KM, Zhang XY, Ren SQ, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[M] ∥Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 2014, 8691: 346- 361.

[24] Ge W Y, Liu G Y. Unsupervised classification of high-resolution remote-sensing images under edge constraints[J]. Proceedings of SPIE, 2017, 10609: 106091C.

袁立, 袁吉收, 张德政. 基于DeepLab-v3+的遥感影像分类[J]. 激光与光电子学进展, 2019, 56(15): 152801. Li Yuan, Jishou Yuan, Dezheng Zhang. Remote Sensing Image Classification Based on DeepLab-v3+[J]. Laser & Optoelectronics Progress, 2019, 56(15): 152801.

本文已被 4 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!