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Robust Cultivated Land Extraction Using Encoder-Decoder

Aziguli Wulamu1,2,*, Jingyue Sang3, Dezheng Zhang1,2, Zuxian Shi1,2

1 Department of Computer, School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, 100083, China
2 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China

* Corresponding Author: Aziguli Wulamu. Email: email

Journal of New Media 2020, 2(4), 149-155. https://doi.org/10.32604/jnm.2020.014115

Abstract

Cultivated land extraction is essential for sustainable development and agriculture. In this paper, the network we propose is based on the encoderdecoder structure, which extracts the semantic segmentation neural network of cultivated land from satellite images and uses it for agricultural automation solutions. The encoder consists of two part: the first is the modified Xception, it can used as the feature extraction network, and the second is the atrous convolution, it can used to expand the receptive field and the context information to extract richer feature information. The decoder part uses the conventional upsampling operation to restore the original resolution. In addition, we use the combination of BCE and Loves-hinge as a loss function to optimize the Intersection over Union (IoU). Experimental results show that the proposed network structure can solve the problem of cultivated land extraction in Yinchuan City.

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APA Style
Wulamu, A., Sang, J., Zhang, D., Shi, Z. (2020). Robust cultivated land extraction using encoder-decoder. Journal of New Media, 2(4), 149-155. https://doi.org/10.32604/jnm.2020.014115
Vancouver Style
Wulamu A, Sang J, Zhang D, Shi Z. Robust cultivated land extraction using encoder-decoder. J New Media . 2020;2(4):149-155 https://doi.org/10.32604/jnm.2020.014115
IEEE Style
A. Wulamu, J. Sang, D. Zhang, and Z. Shi "Robust Cultivated Land Extraction Using Encoder-Decoder," J. New Media , vol. 2, no. 4, pp. 149-155. 2020. https://doi.org/10.32604/jnm.2020.014115



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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