предыдущий|следующий

Extracting winter wheat spatial distribution information from GF-2 image

  • role： First author第一作者
  • Affiliation：

    College of Information Science & Engineering, Shandong Agricultural University, Tai’an271018, China

  • Email：15650099558@163.com
  • Introduction：宋德娟，1994年生,女,硕士研究生, 研究方向为遥感信息提取。E-mail:15650099558@163.com
SONG Dejuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Information Science & Engineering, Shandong Agricultural University, Tai’an271018, China

    Shandong Technology and Engineering Center for Digital Agriculture, Tai’an271018, China

  • Email：chming@sdau.edu.cn
  • Introduction：张承明，1971年生，男，副教授，研究方向为遥感信息提取。E-mail: chming@sdau.edu.cn
ZHANG Chengming12*,  
  • Affiliation：

    College of Information Science & Engineering, Shandong Agricultural University, Tai’an271018, China

YANG Xiaoxia1,  
  • Affiliation：

    Shandong Provincal Climate Center, Jinan250031, China

LI Feng3,  
  • Affiliation：

    Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions， CMA, Yinchuan750002, China

HAN Yingjuan4,  
  • Affiliation：

    The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

GAO Shuai5,  
  • Affiliation：

    College of Information Science & Engineering, Shandong Agricultural University, Tai’an271018, China

DONG Haiyan1

реферат

Winter wheat is one of the main food crops in China. The accurate spatial distribution information of winter wheat is crucial for yield estimation and food security. However, existing methods for extracting the spatial distribution information of winter wheat using a full convolutional neural network ignore the remote sensing imagery characteristics. The influence of differences among probabilities in the coded class probability vector on the judgment of pixel class attribution can lead to misclassification or missing points at the edge, which can affect the accuracy of the result. In this study, the RefineNet model is coupled with maximum a posteriori probability (MAP), and the WWRSE(Winter Wheat Remote Sensing Extraction) model is developed to create a method for extracting the spatial distribution information of winter wheat from a Gaofen-2 remote sensing image.The WWRSE uses the convolution RefineNet network structure to extract pixel features. An improved SOFTMAX model is used to obtain the pixel category probability vector. With the winter wheat category probability value and other categories as a poor probability vector, differences can be divided into small pixels and differences among large pixels according to the category probability vector. For large differences among pixels, the biggest probability category can be used directly as the pixel category. Small differences among pixels can be combined with the MAP model to determine the type of each pixel. Next, the model is trained using the stochastic gradient method, and the spatial distribution information of winter wheat is extracted from the remote sensing image using the successfully trained model.SegNet, DeepLab, and RefineNet were selected as comparison models. Experimental results showed that WWRSE accuracy improved by 4.2%, 7.6%, and 8.6%, compared with the comparison models. Moreover, overall extraction accuracy reached 93%, thereby indicating that the method proposed in this study has certain advantages in proposing the spatial information distribution of winter wheat.This method deeply explores the information contained in the class probability vector of the output of the full convolutional network and determines that the classification error is closely related to the small component difference in the class probability vector. Based on this finding, the classification result of the full convolution network is revised. The effectiveness and feasibility of the proposed method are proven by the experiments.

ключеви́че слова́

GF-2;full convolutional neural networks;RefineNet;maximum aposteriori probability;winter wheat;spatial distribution;Zhangqiu

References

  1. 1.
    Badrinarayanan V, Kendall A and Cipolla R. 2017. SegNet: a deep convolutional encoder-decoder architecture for imagesegmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12): 2481-2495
  2. 2.
    Chen J, Chen T Q, Mei X M, Shao Q B and Deng M. 2014. Hilly farmland extraction from high resolution remote sensing imagery based on optimal scale selection. Transactions of the Chinese Society of Agricultural Engineering, 30(5): 99-107
  3. 3.
    Chen J J and Jin X G. 2015. Binary volume optimization based on maximum a posteriori-Markov random field. Journal of Computer-Aided Design and Computer Graphics, 27(7):1203-1210
  4. 4.
    Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2018a. DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4): 834-848
  5. 5.
    Fan J L and Lei B. 2009. Two-dimensional cross-entropy linear-type threshold segmentation method for gray-level images. Acta Electronica Sinica, 37(3): 476-480
  6. 6.
    Feng M C, Yang W D, Zhang D Y, Cao L L, Wang H F and Wang Q. 2009. Monitoring planting area and growth situation of irrigation-land and dry-land winter wheat based on TM and MODIS data. Transactions of the CSAE, 25(3): 103-109
  7. 7.
    Fu G, Liu C J, Zhou R, Sun T and Zhang Q J. 2017. Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sensing, 9(6): 498
  8. 8.
    Guo W Q, Ren J Q, Liu X R, Chen Z X, Wu S R and Pan H Z. 2018. Winter wheat mapping with globally optimized threshold under total quantiry constraint of statistical data. Journal of Remote Sensing, 22(6): 1023-1041
  9. 9.
    Hao W P, Mei X R,Cai X L, Du J T and Liu Q. 2011. Crop planting extraction based on multi-temporal remote sensing data in Northeast China. Transactions of the CSAE, 27(1): 201-207
  10. 10.
    He H, Zhu X F, Pan Y Z, Zhu W Q, Zhang J S and Jia B. 2008. Study on scale issues in measurement of winter wheat plant area by remote sensing. Journal of Remote Sensing, 12(1): 168-175
  11. 11.
    Hu F, Xia G S, Hu J W and Zhang L P. 2015. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11): 14680-14707
  12. 12.
    Huang J X, Luo Q,Liu X X and Zhang J. 2016. Winter wheat yield forecasting based on time series of MODIS NDVI. Transactions of the Chinese Society for Agricultural Machinery, 47(2): 295-301
  13. 13.
    Li D R, Zhang L P and Xia G S. 2014. Automatic analysis and mining of remote sensing big data. Acta Geodaetica et Cartographica Sinica, 43(12): 1211-1216
  14. 14.
    Lin G S, Milan A, Shen C H and Reid I. 2016. RefineNet: multi-path refinement networks for high-resolution semantic segmentation.arXiv: 1611.06612
  15. 15.
    Lin H N, Shi Z W and Zou Z X. 2017. Maritime semantic labeling of optical remote sensing images with multi-scale fully convolutional network. Remote Sensing, 9(5): 480
  16. 16.
    Liu C, Hong L, Chen J, Chu S S and Deng M. 2015. Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image. Journal of Remote Sensing, 19(2): 228-239
  17. 17.
    Liu D W, Han L and Han X Y. 2016. High spatial resolution remote sensing image classification based on deep learning. Acta Optica Sinica, 36(4): 298-306
  18. 18.
    Mas J F and Flores J J. 2008. The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29(3): 617-663
  19. 19.
    Meng X C, Shen H F, Zhang H Y, Zhang L P and Li H F. 2014. Maximum a posteriori fusion method based on gradient consistency constraint for multispectral/panchromatic remote sensing images. Spectroscopy and Spectral Analysis, 34(5):1332-1337
  20. 20.
    Mountrakis G, Im J and Ogole C. 2011. Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3): 247-259
  21. 21.
    Pringle M J, Denham R J and Devadas R. 2012. Identification of cropping activity in central and southern Queensland, Australia, with the aid of MODIS MOD13Q1 imagery. International Journal of Applied Earth Observation and Geoinformation, 19: 276-285
  22. 22.
    Qian X L, Li J, Cheng G, Yao X W, Zhao S N, Chen Y B and Jiang L Y. 2018. Evaluation of the effect of feature extraction strategy on the performance of high-resolution remote sensing image scene classification. Journal of Remote Sensing, 22(5): 758-776
  23. 23.
    Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention.Munich, Germany:Springer: 234-241
  24. 24.
    Shelhamer E, Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640-651
  25. 25.
    Sun Z R. 2015. Estimation of Winter Wheat Area and Drought Monitoring Research Based on Remote Sensing Data in Beijing-Tianjin-HebeiRegion. Beijing:Beijing Forestry University
  26. 26.
    Visin F, Romero A, Cho K, Matteucci M, Ciccone M, Kastner K, Bengio Y and Courville A. 2016. ReSeg: a recurrent neural network-based model for semantic segmentation//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Las Vegas:IEEE: 426-433
  27. 27.
    Wang J, Song J W, Chen M Q and Yang Z. 2015. Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine. International Journal of Remote Sensing, 36(12): 3144-3169
  28. 28.
    Wang L M, Liu J, Yao B M, Ji F H and Yang F G. 2018. Area change monitoring of winter wheat based on relationship analysis of GF-1 NDVI among different years. Transactions of the Chinese Society of Agricultural Engineering, 34(8): 184-191
  29. 29.
    Wu C L, Chau K W and Fan C. 2010. Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. Journal of Hydrology, 389(1/2: 146-167
  30. 30.
    Wu M Q, Yang L C, Yu B, Wang Y, Zhao X, Niu Z and Wang C Y. 2014. Mapping crops acreages based on remote sensing and sampling investigation by multivariate probability proportional to size. Transactions of the Chinese Society of Agricultural Engineering, 30(2): 146-152
  31. 31.
    Xiao L, Wei Z H and Wu H Z. 2007. A generalized variational image restoration model based on map and robust estimation. Journal of Computer Research and Development, 44(7): 1105-1113
  32. 32.
    Xu Q Y, Yang G J, Long H L, Wang C C, Li X C and Huang D C. 2014. Crop information identification based on MODIS NDVI time-series data. Transactions of the Chinese Society of Agricultural Engineering, 30(11): 134-144
  33. 33.
    You J, Pei Z Y, Wang F, Wu Q and Guo L. 2016. Area extraction of winter wheat at county scale based on modified multivariate texture and GF-1 satellite images. Transactions of the Chinese Society of Agricultural Engineering, 32(13): 131-139
  34. 34.
    Zhang C M, Gao S, Yang X X, Li F, Yue M R, Han Y J, Zhao H, Zhang Y N and Fan K Q. 2018a. Convolutional neural network-based remote sensing images segmentation method for extracting winter wheat spatial distribution. Applied Sciences, 8(10): 1981
  35. 35.
    Zhang C M, Liu J P, Yu F, Wan S J, Han Y J, Wang J and Wang G. 2018b. Segmentation model based on convolutional neural networks for extracting vegetation from Gaofen-2 images. Journal of Applied Remote Sensing, 12(4): 042804
  36. 36.
    Zhang J H, Feng L L and Yao F M. 2014. Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information. ISPRS Journal of Photogrammetry and Remote Sensing, 94: 102-113
  37. 37.
    Zhao L J and Tang P. 2016. Scalability analysis of typical remote sensing data classification methods: a case of remote sensing image scene. Journal of Remote Sensing, 20(2): 157-171

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