YU Xin, ZHENG Zhaobao, MENG Lingkui, LI Linyi. Scene Recognition of Remotely Sensed Images Based on Bayes Adjoint Batch Normalization[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220632
Citation: YU Xin, ZHENG Zhaobao, MENG Lingkui, LI Linyi. Scene Recognition of Remotely Sensed Images Based on Bayes Adjoint Batch Normalization[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220632

Scene Recognition of Remotely Sensed Images Based on Bayes Adjoint Batch Normalization

More Information
  • Received Date: June 03, 2023
  • Available Online: July 02, 2023
  • Objective: Normalization methods plays an important role in feature preprocessing phase not only in conventional machine learning domain but also in contemporary deep learning domain. Batch Normalization (BN) is very successful, but its performance very depends on the sample size. Therefore, many researchers try to improve it when the sample size is inadequate through adding the sample size merely in the sample information space. Methods: This paper utilizes Bayes theory to integrate general information, prior information and sample information, to offset the inadequate sample information. In this way, it is able to estimate sample mean and sample variance more precisely and more robust especially when the sample size is small, and makes the normalized feature better fall into non- saturating region of activation function, which enables deep learning model to better describe original feature space. Results: The top-1 test classification accuracy in the dataset of NWPU-RESISC45 has been improved by 5.64% than BN. Moreover, with the help of general information and prior information, the proposed method(BABN) is not sensitive to the sample size. Conclusions: The experiment results show that the proposed method (Bayes Adjoint Batch Normalization, BABN) is feasible and effective, and the new method performs better in the remotely sensed image scene recognition than Batch Normalization (BN) method and other variants.
  • [1]
    . ZHANG Yongjun, WAN Yi, SHI Wenzhong, ZHANG Zuxun, LI Yansheng, JI Shunping, GUO Haoyu, LI Li. Technical framework and preliminary practices of photogrammetric remote sensing intelligent processing of multi-source satellite images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1068-1083.(张永军, 万一, 史文中, 张祖勋, 李彦胜, 季顺平, 郭浩宇, 李礼. 多源卫星影像的摄影测量遥感智能处理技术框架与初步实践[J]. 测绘学报, 2021, 50(8):1068-1083.)
    [2]
    . SHI Wenzhong, ZHANG Min. Artificial intelligence for reliable object recognition from remotely sensed data:overall framework design, review and prospect[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1049-1058.(史文中, 张敏. 人工智能用于遥感目标可靠性识别:总体框架设计、现状分析及展望[J]. 测绘学报, 2021, 50(8):1049-1058.)
    [3]
    . SHAO Zhenfeng, SUN Yueming, XI Jiangbo, LI Yan. Intelligent Optimization Learning for Semantic Segmentation of High Spatial Resolution Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2022, 47(2):234-241. doi:10.13203/j.whugis20200640.(邵振峰, 孙悦鸣, 席江波, 李岩. 智能优化学习的高空间分辨率遥感影像语义分割[J]. 武汉大学学报(信息科学版), 2022, 47(2):234-241. doi:10.13203/j.whugis20200640)
    [4]
    . GONG Jianya, ZHANG Zhan, JIA Haowei, ZHOU Huan, ZHAO Yuanxin, XIONG Hanjiang. Multi-source Data Ground Object Extraction Based on Knowledge-Aware and Multiscale Feature Fusion Network[J]. Geomatics and Information Science of Wuhan University, 2022, 47(10):1546-1554. doi:10.13203/j.whugis20220580.(龚健雅, 张展, 贾浩巍, 周桓, 赵元昕, 熊汉江. 面向多源数据地物提取的遥感知识感知与多尺度特征融合网络[J]. 武汉大学学报(信息科学版), 2022, 47(10):1546-1554. doi:10.13203/j.whugis20220580)
    [5]
    . YU Xin, ZHENG Zhaobao, LI Linyi. Oblique Factor Model for Selecting Training Samples[J]. Geomatics and Information Science of Wuhan University, 2022, 47(11):1870-1877. doi:10.13203/j.whugis20200631.(虞欣, 郑肇葆, 李林宜. 适用于训练样本选择的斜交因子模 型研 究[J]. 武汉 大学 学报(信息 科学 版), 2022, 47(11):1870-1877. doi:10.13203/j.whugis20200631)
    [6]
    . CHEN Lifu, LONG Fengqi, LI Zhenhong, YUAN Zhihui, ZHU Wu, CAI Xingmin. Multi-level Feature Attention Fusion Network for Water Extraction from Multi-source SAR Images[J]. Geomatics and Information Science of Wuhan University. doi:10.13203/j.whugis20230041. (陈立福, 龙凤琪, 李振洪, 袁志辉, 朱武, 蔡兴敏. 面向多源 SAR图像的多级特征注意力水体提取网络[J]. 武汉大学学报(信息科学版). doi:10.13203/j.whugis20230041)
    [7]
    . LI Yansheng, ZHANG Yongjun. A New Paradigm of Remote Sensing Image Interpretation by Coupling Knowledge Graph and Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8):1176-1190. doi:10.13203/j.whugis20210652.(李彦胜, 张永军. 耦合知识图谱和深度学习的新一代遥感影像解译范式[J]. 武汉大学学报(信息科学版), 2022, 47(8):1176-1190. doi:10.13203/j.whugis20210652)
    [8]
    . LIU Jianwei, ZHAO Huidan, LUO Xionglin, et al. Research progress on batch normalization of deep learning and its related algorithms. Acta Automatica Sinica, 2020, 46(6):1090-1120 doi:10.16383/j.aas.c180564.(刘建伟, 赵会丹, 罗雄麟,等. 深度学习批归一化及其相关算法研究进展[J]. 自动化学报, 2020, 46(6):31.)
    [9]
    . Krizhevsky A, Sutskever I, Hinton G. ImageNet Classification with Deep Convolutional Neural Networks[J]. Advances in neural information processing systems, 2012, 25(2).
    [10]
    . Carandini M, Heeger D J. Normalization as a canonical neural computation[J]. Nature Reviews Neuroscience, 2012, 13(1):51-62.
    [11]
    . Heeger D J. Normalization of cell responses in cat striate cortex[J]. Visual neuroscience, 1992, 9(2):181-197.
    [12]
    . Sergey Ioffe and Christian Szegedy. Batch normalization:Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning, pages 448-456, 2015.
    [13]
    . Peng C, Xiao T, Li Z, et al. Megdet:A large mini-batch object detector[C]//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 2018:6181-6189.
    [14]
    . Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European Conference on Computer Vision (ECCV), pages 3-19, 2018.
    [15]
    . K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers:Surpassing human-level performance on imagenet classification. In ICCV, 2015.
    [16]
    . Gong Cheng, Junwei Han, Xiaoqiang Lu. Remote Sensing Image Scene Classification:Benchmark and State of the Art. Proceedings of the IEEE, 105(10):1865-1883, 2017.
    [17]
    . Guangrun Wang, Ping Luo, Xinjiang Wang, Liang Lin, et al. Kalman normalization:Normalizing internal representations across network layers. In Advances in Neural Information Processing Systems, pages 21-31, 2018.
    [18]
    . Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. Instance normalization:The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022, 2016
    [19]
    . Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.
    [20]
    . Shao W, Meng T, Li J, et al. Ssn:Learning sparse switchable normalization via sparsestmax[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:443-451.
    [21]
    . Li B, Wu F, Weinberger K Q, et al. Positional normalization[J]. Advances in Neural Information Processing Systems, 2019, 32.
    [22]
    . Yao Z, Cao Y, Zheng S, et al. Cross-Iteration Batch Normalization[J]. CVPR 2021.
    [23]
    . Singh S, Krishnan S. Filter Response Normalization Layer:Eliminating Batch Dependence in the Training of Deep Neural Networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020.
    [24]
    . Sergey Ioffe. Batch renormalization:Towards reducing minibatch dependence in batchnormalized models. In Advances in Neural Information Processing Systems, pages 1945-1953, 2017
    [25]
    . Hyeonseob Nam and Hyo-Eun Kim. Batch-instance normalization for adaptively styleinvariant neural networks. In Advances in Neural Information Processing Systems, pages 2563-2572, 2018.
    [26]
    . Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, and Alan Yuille. Weight standardization. arXiv preprint arXiv:1903.10520, 2019.
    [27]
    . S. Gross and M.Wilber. Training and investigating Residual Nets. https://github.com/facebook/fb.resnet.torch,2016.
    [28]
    . C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, 2015.
    [29]
    . Xiao-Yun Zhou, Jiacheng Sun, Nanyang Ye, Xu Lan, Qijun Luo, Bo-Lin Lai, Pedro Esperanca, Guang-Zhong Yang, and Zhenguo Li. Batch group normalization. arXiv preprintarXiv:2012.02782, 2020.
    [30]
    . Huang L, Yang D, Lang B, et al. Decorrelated batch normalization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:791-800.
    [31]
    . De Vries H, Strub F, Mary J, et al. Modulating early visual processing by language[J]. Advances in Neural Information Processing Systems, 2017, 30.
    [32]
    . Singh S, Shrivastava A. Evalnorm:Estimating batch normalization statistics for evaluation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019:3633-3641.
    [33]
    . Jia S, Chen D J, Chen H T. Instance-level meta normalization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:4865-4873.
    [34]
    . Gülçehre Ç, Bengio Y. Knowledge matters:Importance of prior information for optimization[J]. The Journal of Machine Learning Research, 2016, 17(1):226-257.
    [35]
    . Arpit D, Zhou Y, Kota B, et al. Normalization propagation:A parametric technique for removing internal covariate shift in deep networks[C]//International Conference on Machine Learning. PMLR, 2016:1168-1176.
    [36]
    . Ren M, Liao R, Urtasun R, et al. Normalizing the normalizers:Comparing and extending network normalization schemes[J]. arXiv preprint arXiv:1611.04520, 2016.
    [37]
    . Gong X, Chen W, Chen T, et al. Sandwich Batch Normalization:A Drop-In Replacement for Feature Distribution Heterogeneity[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022:2494-2504.
    [38]
    . Miyato T, Kataoka T, Koyama M, et al. Spectral normalization for generative adversarial networks[J]. arXiv preprint arXiv:1802.05957, 2018.
    [39]
    . Liao Q, Kawaguchi K, Poggio T. Streaming normalization:Towards simpler and more biologically-plausible normalizations for online and recurrent learning[J]. arXiv preprint arXiv:1610.06160, 2016.
    [40]
    . Luo P, Peng Z, Ren J, et al. Do normalization layers in a deep ConvNet really need to be distinct?[J]. arXiv preprint arXiv:1811.07727, 2018.
    [41]
    . Salimans T, Kingma D P. Weight normalization:A simple reparameterization to accelerate training of deep neural networks[J]. Advances in neural information processing systems, 2016, 29.
    [42]
    . China Statistics Press. 2012.9(峁诗松著. 贝叶斯统计(第2版). 中国统计出版社. 2012.9)

    . Shisong Mao. Bayesian Statistics (second edition)
    [43]
    . FENG Quanlong, CHEN Boan, LI Guoqing, YAO Xiaochuang, GAO Bingbo and ZHANG Lianchong. 2022. A review for sample datasets of remote sensing imagery. National Remote Sensing Bulletin, 26(4):589-605.(冯权泷,陈泊安,李国庆,姚晓闯,高秉博,张连翀. 遥感影像样本数据集研究综述[J]. 遥感学报,2022,26(04):589-605.)
    [44]
    . GONG Jianya, XU Yue, HU Xiangyun, JIANG Liangcun, ZHANG Mi. Status analysis and research of sample database for intelligent interpretation of remote sensing image[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1013-1022.(龚健雅, 许越, 胡翔云, 姜良存, 张觅. 遥感影像智能解译样本库现状与研究[J]. 测绘学报, 2021, 50(8):1013-1022.)
    [45]
    . ZHONG Shouyi,XIAO Qing,WEN Jianguang,ZHENG Xingming,MA Mingguo,QU Yonghua,ZHENG Ke,CHI Tianhe,TANG Yong,YOU Dongqin,et al. 2020. Design and realization of ground object background spectral library for surveying and mapping. Journal of Remote Sensing(Chinese), 24(6):701-716.(钟守熠,肖青,闻建光,郑兴明,马明国,屈永华,郑柯,池天河,唐勇,游冬琴,郝大磊,程娟,贺敏,姜涛,晋锐,姚晓婧,赵理君.2020.测绘地物波谱本底数据库.遥感学报,24(6):701-716.)
    [46]
    . Xia G S, Hu J W, Hu F, Shi B G, Bai X, Zhong Y F, Zhang L P and Lu X Q. 2017. AID:a benchmark data set for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7):3965-3981[DOI: 10.1109/tgrs.2017.2685945]
    [47]
    . Helber P, Bischke B, Dengel A and Borth D. 2019. EuroSAT:a novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7):2217-2226[DOI: 10.1109/jstars.2019.2918242]
    [48]
    . Basu S, Ganguly S, Mukhopadhyay S, DiBiano R, Karki M and Nemani R. 2015. DeepSat:a learning framework for satellite imagery//Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, Washington:ACM[DOI: 10.1145/2820783.2820816]
    [49]
    . J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet:A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009
    [50]
    . TAO Chao, YIN Ziwei, ZHU Qing, LI Haifeng. Remote sensing image intelligent interpretation:from supervised learning to self-supervised learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1122-1134.(陶超, 阴紫薇, 朱庆, 李海峰. 遥感影像智能解译:从监督学习到自监督学习[J]. 测绘学报, 2021, 50(8):1122-1134.)
  • Related Articles

    [1]LI Haifeng, LUO Qinyao, HE Silu, REN Zhen, LIU Yu. Geospatial Causal Principle and Causal Discovery for Geospatial Effects[J]. Geomatics and Information Science of Wuhan University, 2023, 48(11): 1800-1812. DOI: 10.13203/j.whugis20230351
    [2]LIU Shuang, HU Xiangyun, GUO Ning, CAI Hongzhu, ZHANG Henglei, LI Yongtao. Overview on UAV Aeromagnetic Survey Technology[J]. Geomatics and Information Science of Wuhan University, 2023, 48(6): 823-840. DOI: 10.13203/j.whugis20220623
    [3]JIN Shaohua, LI Jiabiao, WU Ziyin, BIAN Gang, CUI Yang. Estimation of Spacing of Survey Line Layout with EGM2008 Model in Marine Gravity Survey[J]. Geomatics and Information Science of Wuhan University, 2018, 43(9): 1315-1319. DOI: 10.13203/j.whugis20160394
    [4]QI Jun, BAO Jingyang, LIU Yanchun. Determination of Barycenter of Surveying Boat in Hydrographic Survey[J]. Geomatics and Information Science of Wuhan University, 2010, 35(9): 1048-1051.
    [5]ZHANG Zuxun. On Informatization of Surveying and Mapping from the Development of Digital Photogrammetry[J]. Geomatics and Information Science of Wuhan University, 2008, 33(2): 111-115.
    [6]HUANG Motao, ZHAI Guojun, OUYANG Yongzhong, REN Laiping. On Error Compensation in Marine Magnetic Survey[J]. Geomatics and Information Science of Wuhan University, 2006, 31(7): 603-606.
    [7]WANG Xinzhou, LU Jiaju, HUA Xianghong, LIU Xianglin. The Research and Establishment of the Surveying Ensurement System in Wuhan No.1 Rail Transit Engineering[J]. Geomatics and Information Science of Wuhan University, 2002, 27(3): 265-269.
    [8]WANG Xinzhou. The Comprehensive Model of Generalized Surveying Adjustment[J]. Geomatics and Information Science of Wuhan University, 2000, 25(3): 257-260.
    [9]Wang Zhizhuo. From "Surveying and Mapping" to "Geomatics"[J]. Geomatics and Information Science of Wuhan University, 1998, 23(4): 294-296.
    [10]Peng Xian jin. On optimal Design of Survey Networks[J]. Geomatics and Information Science of Wuhan University, 1993, 18(3): 89-94.

Catalog

    Article views (305) PDF downloads (13) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return