سابِق|تال

Object-oriented crop classification for GF-6 WFV remote sensing images based on Convolutional Neural Network

  • role： First author第一作者
  • Affiliation：

    College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China

  • Email：15735517540@163.com
  • Introduction：李前景，1995年生，男，硕士研究生，研究方向为遥感影像分类及信息提取。E-mail: 15735517540@163.com
LI Qianjing1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China

  • Email：8886355@163.com
  • Introduction：刘珺，1981年生，女，讲师，研究方向为资源环境遥感，多光谱遥感应用。E-mail: 8886355@163.com
LIU Jun1*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

MI Xiaofei2,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

YANG Jian2,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

YU Tao2

ملخص

The GF-6 WFV image is the first remote sensing image of the 8-band multi-spectral satellite with medium and high resolution in China. 4 spectrum bands including two red-edge bands are added to the image based on the conventional red, green, blue and near-infrared band. As a vegetation sensitive band, the red-edge band is one of the methods used for crop classification and identification in remote sensing images. Research on the impact of GF-6 WFV image and its red-edge bands on crop classification is urgently needed.This research uses GF-6 WFV images as the data source. The main work is (1) proposes a convolutional neural network (RE-CNN) remote sensing image crop classification model suitable for GF-6 WFV red-edge bands; (2) conducts crop classification research about GF-6 WFV imagery and its red-edge bands and evaluates effectiveness of red-edge bands due to the lack of relevant research, (3) uses the strategy of combining object-oriented and deep learning for crop classification. The core idea of this research: multi-scale segmentation is used in order to avoid the influence of salt and pepper phenomenon on image classification, and image segmentation is completed by selecting the best segmentation parameters with ESP tools and ROC-LV. Object-oriented classification by CART decision tree can extract vegetation area while eliminating salt and pepper noise, and convert it into input data of convolutional neural network. The network structure of Inception was introduced to extract the multi-scale features of the image and then a convolutional neural network model (RE-CNN) for GF-6 WFV imagery was constructed for crop classification. A control experimental group with or without red-edge bands was set up and the RE-CNN model was used for crop classification and accuracy verification. The effect of the newly added red-edge bands on crop classification is studied, and the effectiveness and sensitivity of the red-edge band are evaluated in crop classification by GF-6 WFV imagery.The experimental results of this study show that: (1) Object-oriented CART decision tree classification effectively eliminates salt and pepper noise in vegetation area extraction, and the classification strategy of combining with deep learning achieve better classification results in remote sensing image crop classification. (2) The RE-CNN model proposed in this paper can be used for GF-6 WFV remote sensing image crop classification. The classification accuracy of the experimental group in the group of red-edge bands is as high as 94.38%, and the Kappa coefficient is 0.92. (3) The newly added red-edge bands in GF-6 WFV images can effectively improve the crop classification accuracy of remote sensing image. Compared with the group without red-edge bands, the classification accuracy is increased by 2.83%, which verifies the effectiveness and sensitivity that the newly-added red-edge bands in GF-6 WFV images improves the classification accuracy. Moreover, it provides a reference for the research of GF-6 WFV image and its red-edge band for crop classification.

مفهوم

remote sensing;object-oriented;classification;GF-6;Red-Edge band;convolutional neural network

References

  1. 1.
    Benz U C, Hofmann P, Willhauck G, Lingenfelder I and Heynen M. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3/4): 239-258
  2. 2.
    Breiman L. 1984. Classification and Regression Trees. New York: Routledge
  3. 3.
    Chang C Y, Zhao G X, Wang L, Zhu X C and Gao Z. 2012. Land use classification based on RS object-oriented method in coastal spectral confusion region. Transactions of the Chinese Society of Agricultural Engineering, 28(5): 226-231
  4. 4.
    Cheng G L, Wang Y, Xu S B, Wang H Z, Xiang S M and Pan C H. 2017. Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 55(6): 3322-3337
  5. 5.
    Dong Z, Pei M T, He Y, Liu T, Dong Y M and Jia Y D. 2014. Vehicle type classification using unsupervised convolutional neural network//Proceedings of 2014 22nd International Conference on Pattern Recognition. Stockholm, Sweden: IEEE: 172-177
  6. 6.
    Drǎguţ L, Tiede D and Levick S R. 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859-871
  7. 7.
    Fu X L, Li L P, Mao K B, Tan X L, Li J J, Sun X and Zuo Z Y. 2017. Remote sensing image classification based on CNN model. Chinese High Technology Letters, 27(3): 203-212
  8. 8.
    Glorot X, Bordes A and Bengio Y. 2011. Deep sparse rectifier neural networks. Journal of Machine Learning Research, 15: 315-323
  9. 9.
    Horler D N H, Dockray M and Barber J. 1983. The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2): 273-288
  10. 10.
    Huang B, Zhao B and Song Y M. 2018. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment, 214: 73-86
  11. 11.
    Kim H O and Yeom J M. 2014. Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data. International Journal of Remote Sensing, 35(19): 7046-7068
  12. 12.
    Kussul N, Lavreniuk M, Skakun S and Shelestov A. 2017. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5): 778-782
  13. 13.
    LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature, 521(7553): 436-444
  14. 14.
    Ma K and Luo Z. 2018. Classification of remote sensing images in Qinghai lake based on convolutional neural network. Computer Systems and Applications, 27(9): 137-142
  15. 15.
    Murthy C S, Raju P V and Badrinath K V S. 2003.Classifica- tion of wheat crop with multi-temporal images: perform- ance of maximum likelihood and artificial neural netw- orks. International Journal of Remote Sensing, 24(23): 4871-4890
  16. 16.
    Othman E, Bazi Y, Alajlan N, Hichri H and Melgani F. 2016. Using convolutional features and a sparse autoencoder for land-use scene classification. International Journal of Remote Sensing, 37(10): 2149-2167
  17. 17.
    Peng G X, Gong A D, Cui W H, Ming T and Chen F R. 2009. Study on methods comparison of typical remote sensing classification based on multi-temporal images. Journal of Geo-information Science, 11(2): 225-230
  18. 18.
    Verburg H P, Neumann K and Nol L. 2011. Challenges in using land use and land cover data for global change studies. Global Change Biology, 17(2): 974-989
  19. 19.
    Wang J L, Huang J L, Wang L H, Hu Y X, Han P P and Huang W. 2014. Identification of sugarcane based on object-oriented analysis using time-series HJ CCD data. Transactions of the Chinese Society of Agricultural Engineering, 30(11): 145-151
  20. 20.
    Wardlow B D, Egbert S L and Kastens J H. 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment, 108(3): 290-310
  21. 21.
    Wu J S, Pan K Y, Peng J and Huang X L. 2012. Research on the accuracy of TM images land-use classification based on QUEST decision tree: a case study of Lijiang in Yunnan. Geographical Research, 31(11): 1973-1980
  22. 22.
    Yan J L, Zhou W Q, Han L J and Qian Y G. 2018. Mapping vegetation functional types in urban areas with WorldView-2 imagery: integrating object-based classification with phenology. Urban Forestry and Urban Greening, 31: 230-240
  23. 23.
    Zhang C, Sargent I, Pan X, Li H P, Gardiner A, Hare J and Atkinson P M. 2019. Joint Deep Learning for land cover and land use classification. Remote Sensing of Environment, 221: 173-187
  24. 24.
    Zhang K, Hei B Q, Zhou Z and Li S Y. 2018. CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification. Journal of Remote Sensing, 22(1): 87-96
  25. 25.
    Zhang R, Li W P and Mo T. 2018. Review of deep learning. Information and Control, 47(4): 385-397, 410 (张荣, 李伟平, 莫同. 2018. 深度学习研究综述. 信息与控制, 47(4): 385-397, 410)
  26. 26.
    Zhao P, Fu Y F, Zheng L G, Feng X Z and Satyanarayana. 2005. Cart-based land use/cover classification of remote sensing images. Journal of Remote Sensing, 9(6): 708-716
  27. 27.
    Zhu Y S, Zeng Y N and Zhang M. 2017. Extract of land use/cover information based on HJ satellites data and object-oriented classification. Transactions of the Chinese Society of Agricultural Engineering, 33(14): 258-265

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website