Früher|Nächster

Feature extraction and recognition of erosion gully based on remote sensing image in the black soil region in Northeast China

  • role： First author第一作者
  • Email：yupeixin19921227@126.com
  • Introduction：于佩鑫，1992年生，女，硕士研究生，研究方向为植被遥感和遥感图像分类。E-mail： yupeixin19921227@126.com
Peixin YU,  
  • No information about the author is available
Xun ZHOU,  
  • role： Corresponding author通信作者
  • Email：liush@bnu.edu.cn
  • Introduction：刘素红，1967年生，女，教授，研究方向为植被遥感和遥感图像解译。E-mail： liush@bnu.edu.cn
Suhong LIU*,  
  • No information about the author is available
Xikai WANG

Resümee

Northeast China is a major commodity grain base in China. The protection of cultivated lands in Northeast China is crucial for safeguarding the food security in China. The recognition of erosion gullies is an important means of monitoring soil erosion. Furthermore, remote sensing technology is extensively used in this field given the multiple advantages of this technology. However, the traditional methods based on remote sensing mostly depend on manual interpretations. Therefore, the degree of the automation and the efficiency are relatively low. In this study, multi-level features were extracted, thereby effectively describing the specific objects by using machine and deep learnings, and erosion gullies were identified based on these features to improve the accuracy and efficiency of recognizing erosion gullies. In this study, we first cut the remote sensing images in a fixed size and labeled these images manually to create datasets as training samples that consist of two categories, namely, farmland and erosion gully. Second, we extracted spectral and textural features based on this dataset as low-level features, encoded SIFT features through ScSPM as middle-level features, and extracted high-level features by using CNN. Third, a linear SVM and a softmax classifier were applied to classify the remote sensing images based on the multi-level features to identify the images with erosion gullies. Finally, we completed a set of methods to extract the feature and recognize the erosion gully, thereby providing a robust support for protecting arable land in the black soil area of Northeast China. The multi-level features extracted through the proposed method demonstrate specific capabilities in identifying erosion gully images. In the test phase, results show that the recognition outcome based on low-level features exhibits the lowest accuracy (91.1%), whereas the recognition accuracy based on middle-level features is the highest (98.5%). However, both features require a manual design. Hence, the degree of automation is limited. By contrast, the CNN can extract high-level features and automatically achieve an “end-to-end” learning, which highly improves the degree of automation of erosion gully recognition. Furthermore, the recognition accuracy based on high-level features is 95.5%, which satisfies the expectation of this study. The recognition accuracy is slightly lower in the validation phase than in the test phase because the images typically contain several irrelevant objects in the practical application, thereby preventing the improvement of the accuracy. However, the proposed method can generally identify the erosion gullies in the images with a reasonable practicality. Low-level features demonstrate several advantages, such as simple calculation and low time consumed. However, the capability to describe the erosion gully is relatively poor, thus resulting in low recognition accuracy. By contrast, the methods based on middle- and high-level features can identify nearly all the erosion gullies in the images, although these methods are time-consuming during the early training phase. Specifically, the method based on high-level features can automatically recognize the erosion gully. This study shows that deep learning has a great potential in remote sensing image application. If the sample size is continuously increased and the network structure expanded, then the recognition accuracy of erosion gully can be further improved.

Schlüsselwort

erosion gullies;training samples;low-level features;middle-level features;high-level features;convolution neural network

References

  1. 1.
    Blaschke T and Strobl J. 2001. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GIS Zeitschrift für Geoinformationssysteme, 6(1): 12–17
  2. 2.
    Chen L J, Yang W, Xu K and Xu T. 2011. Evaluation of local features for scene classification using VHR satellite images//Proceedings of 2011 Joint Urban Remote Sensing Event. Munich, Germany: IEEE: 385–388 [DOI: 10.1109/JURSE.2011.5764800]
  3. 3.
    Cheng Y Q and Zhang P Y. 2005. Regional patterns changes of Chinese grain production and response of commodity grain base in northeast China. Scientia Geographica Sinica, 25(5): 513–520 (
  4. 4.
    Fan H M, Wang T L, Cai Q G, Guo C J, Wu M and Zhou L L. 2007. Gully erosion developing model in the gentle hilly black soil regions in Northeast China. Research of Soil and Water Conservation, 14(6): 384–387 (
  5. 5.
    Gualtieri J A and Chettri S. 2000. Support vector machines for classification of hyperspectral data//IEEE 2000 International Geoscience and Remote Sensing Symposium. Honolulu, HI: IEEE, 2: 813–815
  6. 6.
    Haralick R M, Shanmugam K and Dinstein I. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6): 610–621
  7. 7.
    Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Advances in Neural Information Processing Systems. Lake Tahoe: NIPS: 1097–1105
  8. 8.
    Lazebnik S, Schmid C and Ponce J. 2006. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories//Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, NY: IEEE: 2169–2178 [DOI: 10.1109/CVPR.2006.68]
  9. 9.
    Li X W and Lin P. 2001. The theoretical basis and progress: utility and protection of the farmer-land in foreign and home. Progress in Geography, 20(4): 305–312 (
  10. 10.
    Lowe D G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91–110
  11. 11.
    Luo B, Jiang S J and Zhang L P. 2013. Indexing of remote sensing images with different resolutions by multiple features. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(4): 1899–1912
  12. 12.
    McInnes J, Vigiak O and Roberts A M. 2011. Using Google earth to map gully extent in the west Gippsland region (Victoria, Australia)//Proceedings of the 19th International Congress on Modelling and Simulation. Perth, Australia: MODSIM: 3370–3376
  13. 13.
    Metternicht G I and Zinck J A. 1998. Evaluating the information content of JERS-1 SAR and Landsat TM data for discrimination of soil erosion features. ISPRS Journal of Photogrammetry and Remote Sensing, 53(3): 143–153
  14. 14.
    Shruthi R B V, Kerle N and Jetten V. 2011. Object-based gully feature extraction using high spatial resolution imagery. Geomorphology, 134(3/4): 260–268
  15. 15.
    Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556
  16. 16.
    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, MA: IEEE: 1–9 [DOI: 10.1109/CVPR.2015.7298594]
  17. 17.
    Xia G S, Hu J W, Hu F, Shi B G, Bai X, Zhong Y F and Zhang L P. 2016. AID: a benchmark data set for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3965–3981
  18. 18.
    Yan Y C, Zhang S W, Li X Y, and Yue S P. 2005. Temporal and spatial variation of erosion gullies in Kebai black soil region of Heilongjiang during the past 50 years. Acta Geographica Sinica, 60(6): 1015–1020 (
  19. 19.
    Yan Y C, Zhang S W and Yue S P. 2007. Classification of erosion gullies by remote sensing and spatial pattern analysis in black soil region of eastern Kebai. Scientia Geographica Sinica, 27(2): 193–199 (
  20. 20.
    Yang Y and Newsam S. 2010. Bag-of-visual-words and spatial extensions for land-use classification//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose, California: ACM: 270–279 [DOI: 10.1145/1869790.1869829]
  21. 21.
    Yu K, Yang J C and Gong Y H. 2013. Linear Spatial Pyramid Matching Using Sparse Coding. US, US8374442B2.

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