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Arable Land Change Detection Using Landsat Data and Deep Learning

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Book cover Artificial Intelligence (CICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13069))

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Abstract

Arable land is closely related to people’s livelihood. Protecting arable land is very urgent. Thus, rapid and accurate detection of arable land changes is especially important for arable land protection. However, most existing deep learning-based methods can easily lead to the accumulation of errors, low accuracy, and have poor anti-noise ability. In this study, we proposed an improved U-Net model for arable land change detection. This is an end-to-end network that is briefer and more intuitive. The model was trained and tested on three arable land areas in Xinjiang. We trained Landsat 8 images of exuberant arable land areas with RGB and 15 m spatial resolution. The improved U-Net model has some advantages compared to other methods: the deeper U-Net has a larger field of perception, with greater noise immunity, and deep convolution can capture more complex spectral features, thus improving feature differentiation. Considering that the deeper the network, the easier the gradient disappears, we use residual units to prevent gradients from disappearing. Moreover, the model parameters were adjusted to reduce the complexity of the model. The experimental results show superior performance on change detection tasks compared to other traditional models with 96.00% accuracy, precision, recall, and FI score, 93.54%, 85.07%, 88.29%. Through experiments, we found that the network can detect the change of cultivated land well. Thus, the proposed model can effectively implement arable land change detection.

This research was funded by [the National Natural Science Foundation of China] grant number [No. U1603115], [the National Key Research and Development Program of China] grant number [No. 2017YFBO504203], [the Science and Technology Planning Project of Sichuan Province] grant number [No. 18SXHZ0054] and [National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data] grant number [PSRPC: No. XJ201810101].

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Authors and Affiliations

  1. School of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

    Mei Huang & Wenzhong Yang

Authors
  1. Mei Huang
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  2. Wenzhong Yang
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Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Duke University, Durham, NC, USA

    Yiran Chen

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. University of British Columbia, Vancouver, BC, Canada

    Jane Wang

  5. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

  6. Xidian University, Xi’an, China

    Weisheng Dong

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Huang, M., Yang, W. (2021). Arable Land Change Detection Using Landsat Data and Deep Learning. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_49

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  • DOI: https://doi.org/10.1007/978-3-030-93046-2_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93045-5

  • Online ISBN: 978-3-030-93046-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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