Précédent|Prochain

High-resolution cropland extraction in Shandong province using MPSPNet and UNet network

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China

  • Email：lqn@mail.bnu.edu.cn
  • Introduction：李倩楠， 研究方向为农业遥感。E-mail： lqn@mail.bnu.edu.cn
LI Qiannan13,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China

ZHANG Dujuan13,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

    Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China

  • Email：pyz@bnu.edu.cn
  • Introduction：潘耀忠，研究方向为农业遥感。E-mail： pyz@bnu.edu.cn
PAN Yaozhong123*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China

DAI Jiajia13

résumé

The rapid development of remote sensing image technology enables a large number of high-resolution remote sensing images to provide good data support for the accurate extraction of cropland and other ground features. However, high-resolution remote sensing images have large data volume and complex features, the artificial visual interpretation and traditional classification methods have limited extraction capabilities which cannot realized large-scale high-precision cropland extraction automatically. Deep learning technology has shown superior performance in the automatic extraction of remote sensing image information due to its strong ability to express features, providing a new idea for the automatic extraction of large-scale cropland. Exploring the application of different typical network models in the extraction of cropland with different landscape features is of great significance to the improvement of the quality and efficiency of cropland extraction. Based on above, the study uses the 2 m resolution data fused with GF-1 and GF-2 in 2015—2017 as the data source. Using Modified Pyramid Scene Parsing Network (MPSPNet) and UNet models applied to the fine automatic extraction of cropland in Shandong Province, and compared with the traditional object-oriented method, exploring the applicability of two deep convolutional neural network models in the automatic extraction of large-scale cropland. We also apply the trained models to the images of different regions and different time phases for the extraction of cropland, and explore the generalization ability of the models. The landscape features of cropland and uncertainty results are analyzed to explore the factors affecting the accuracy of cropland extraction by the models. Results show that: (1) MPSPNet and UNet models perform better than traditional object-oriented classification methods in the extraction of cropland at the district/county scale, the overall accuracy of the extraction of cropland at the provincial scale is better than 90% and there is no obvious difference between two models. (2) The landscape characteristic of cropland is an important factor that affects the effect of the two models, and the choice of the model has no obvious influence on the cropland extraction effect. The extraction effect is better in areas where the cropland landscape index is low and the plots are regular and flat, and the extraction effect is poor in the broken hilly areas of the plots with high cropland landscape index and in the noncropland plots whose characteristics are similar to the cropland, the UNet model is more likely to misclassify cropland in these areas. (3) The two models can obtain better cropland extraction effects in images of different regions and different time phases, and have strong generalization capabilities and temporal and spatial migration capabilities. This study proves the powerful feature learning capabilities of MPSPNet and UNET network models for high-resolution images, and the application potential of deep learning algorithms in fully automatic high-resolution cropland extraction.

mots-clés

cropland;remote sensing;convolutional neural network;MPSPNet;UNet

References

  1. 1.
    Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16
  2. 2.
    Bruzzone L., Carlin L., 2006. A multilevel context-based system for classification of very high spatial resolution images. IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2587-2600
  3. 3.
    Chen L C, Zhu Y K, Papandreou G, Schroff F and Adam H. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation//15th European Conference on Computer Vision. Munich: Springer: 833-851
  4. 4.
    Cheng G, Han J W and Lu X Q. 2017. Remote sensing image scene classification: benchmark and state of the art. Proceedings of the IEEE, 105(10): 1865-1883
  5. 5.
    Clayton D G. 1971. Gram-Schmidt orthogonalization. Journal of the Royal Statistical Society: Series C (Applied Statistics), 20(3): 335-338
  6. 6.
    Costa H, Foody G M and Boyd D S. 2017. Using mixed objects in the training of object-based image classifications. Remote Sensing of Environment, 190: 188-197
  7. 7.
    Dehghan H and Ghassemian H. 2006. Measurement of uncertainty by the entropy: application to the classification of MSS data. International Journal of Remote Sensing, 27(18): 4005-4014
  8. 8.
    De Pinho C M D, Fonseca L M G, Korting T S, De Almeida C M and Kux H J H. 2012. Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis. International Journal of Remote Sensing, 33(19): 5973-5995
  9. 9.
    Goodin D G, Anibas K L and Bezymennyi M. 2015. Mapping land cover and land use from object-based classification: an example from a complex agricultural landscape. International Journal of Remote Sensing, 36(18): 4702-4723
  10. 10.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 770-778
  11. 11.
    He Z, Liu H, Wang Y W and Hu J. 2017. Generative adversarial networks-based semi-supervised learning for hyperspectral image classification. Remote Sensing, 9(10): 1042
  12. 12.
    Hernandez I E R and Shi W Z. 2018. A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis. International Journal of Remote Sensing, 39(4): 1175-1198
  13. 13.
    Hu Q, Wu W B, Song Q, Yu Q Y, Yang P and Tang H J. 2015. Recent progresses in research of crop patterns mapping by using remote sensing. Scientia Agricultura Sinica, 48(10): 1900-1914
  14. 14.
    Jia Y Q, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S and Darrell T. 2014. Caffe: convolutional architecture for fast feature embedding. arXiv: 1408.5093
  15. 15.
    Julien Y, Sobrino J A and Jiménez-Muñoz J C. 2011. Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method. International Journal of Applied Earth Observation and Geoinformation, 13(5): 711-720
  16. 16.
    Kingma D P and Ba J. 2017. Adam: a Method for Stochastic Optimization. arXiv:1412.6980
  17. 17.
    Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe: Curran Associates Inc.: 1097-1105
  18. 18.
    Laben C A and Brower B V. 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. U.S., 6011875
  19. 19.
    Larochelle H, Bengio Y, Louradour J and Lamblin P. 2009. Exploring strategies for training deep neural networks. The Journal of Machine Learning Research, 10: 1-40
  20. 20.
    LeCun Y, Boser B, Denker J S, Henderson D, Howard R E, Hubbard W and Jackel L D. 1989. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4): 541-551
  21. 21.
    LeCun Y, Bottou L, Bengio Y and Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278-2324
  22. 22.
    Li C J, Huang H and Li W. 2018. Research on agricultural remote sensing image cultivated land extraction technology based on support vector machine. Instrumentation Technology, 11: 5-8, 48
  23. 23.
    Li P, Yu H, Wang P and Li K Y. 2017. Comparison and analysis of agricultural information extraction methods based upon GF2 satellite images. Bulletin of Surveying and Mapping, (1): 48-52
  24. 24.
    LIU Wei, WU Zhifeng, LUO Jiancheng, et al. A divided and stratified extraction method of high-resolution remote sensing information for cropland in hilly and mountainous areas based on deep learning[J].Acta Geodaetica et Cartographica Sinica, 2021,50(1):105-116
  25. 25.
    Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE: 3431-3440
  26. 26.
    Lu H, Fu X, Liu C, Li L G, He Y X and Li N W. 2017. Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning. Journal of Mountain Science, 14(4): 731-741
  27. 27.
    Lu S, Oki K, Shimizu Y and Omasa K. 2007. Comparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data. International Journal of Remote Sensing, 28(5): 963-984
  28. 28.
    Ma L, Li M C, Ma X X, Cheng L, Du P J and Liu Y X. 2017. A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 277-293
  29. 29.
    Ma L, Liu Y, Zhang X L, Ye Y X, Yin G F and Johnson B A. 2019. Deep learning in remote sensing applications: a meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152: 166-177
  30. 30.
    Phalke A R and Özdoğan M. 2018. Large area cropland extent mapping with Landsat data and a generalized classifier. Remote Sensing of Environment, 219: 180-195
  31. 31.
    Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer: 234-241
  32. 32.
    Shu Y, Tang H, Li J, Mao T, He S, Gong A D, Chen Y H and Du H Y. 2015. Object-based unsupervised classification of VHR panchromatic satellite images by combining the HDP and IBP on multiple scenes. IEEE Transactions on Geoscience and Remote Sensing, 53(11): 6148-6162
  33. 33.
    Simonyan K and Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
  34. 34.
    Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A. 2015. Going deeper with convolutions//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE: 1-9
  35. 35.
    Waldner F, Canto G S and Defourny P. 2015. Automated annual cropland mapping using knowledge-based temporal features. ISPRS Journal of Photogrammetry and Remote Sensing, 110: 1-13
  36. 36.
    Wang Q. 2021. Progress of environmental remote sensing monitoring technology in China and some related frontier issues. National Remote Sensing Bulletin, 25(1): 25-36
  37. 37.
    Wang W H, Xia L G, Luo J C and Hu X D. 2011. An iterative approach to object-oriented classification of remotely sensed image. Geomatics and Information Science of Wuhan University, 36(10): 1154-1158
  38. 38.
    Xu W N. 2020. Research on Cultivated Land Extraction Technology of Remote Sensing Images based on High Resolution Full Convolutional Network. Shenzhen: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
  39. 39.
    Zhang C, Sargent I, Pan X, Li H P, Gardiner A, Hare J and Atkinson P M. 2018. An object-based convolutional neural network (OCNN) for urban land use classification. Remote Sensing of Environment, 216: 57-70
  40. 40.
    Zhang D J, Pan Y Z, Zhang J S, Hu T G, Zhao J H, Li N and Chen Q. 2020. A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution. Remote Sensing of Environment, 247: 111912
  41. 41.
    Zhang F, Zhao Z G, Li G and Chen G. 2019. Study on classification and extraction of agricultural land in Qitai County of Xinjiang based on different classifiers. Xinjiang Agricultural Sciences, 56(8): 1560-1568
  42. 42.
    Zhang M and Huang S Y. 2019. Remote sensing image classification based on Landsat-8. Geomatics and Spatial Information Technology, 42(1): 177-180
  43. 43.
    Zhao H S, Shi J P, Qi X J, Wang X G and Jia J Y. 2017. Pyramid scene parsing network//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE: 6230-6239
  44. 44.
    Zhou P C, Cheng G, Yao X W and Han J W. 2021. Machine learning paradigms in high-resolution remote sensing image interpretation. National Remote Sensing Bulletin, 25(1): 182-197

Lire l'article complet

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