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Wheat acreage retrieval based on self-supervised learning-based spectral unmixing algorithm

  • role： First author第一作者
  • Affiliation：

    Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

    School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China

  • Email：ltpai91@hotmail.com
  • Introduction：胡金龙，研究方向为定量遥感和点云信息提取。E-mail： ltpai91@hotmail.com
HU Jinlong12,  
  • Affiliation：

    Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

DUAN Jinliang1,  
  • Affiliation：

    Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

SHAMA Age1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

    State-Province Joint Engineering Laboratory of Spatial Information Technology of High-Speed Rail Safety, Southwest Jiaotong University, Chengdu 611756, China

  • Email：zhangrui@swjtu.edu.cn
  • Introduction：张瑞，研究方向为SAR图像处理和定量遥感。E-mail： zhangrui@swjtu.edu.cn
ZHANG Rui13*

résumé

Wheat is an important food crop in China, and its distribution acquisition and planting area estimation are crucial to ensuring national food security and development planning. As a powerful tool for large-scale land cover extraction and temporal and spatial dynamic monitoring, remote sensing has remarkable advantages in the field of wheat planting area estimation. However, wheat is often mixed with other ground objects and forms mixed pixels limited by the spatial resolution of remote sensing images and the influence of various factors in the transmission of electromagnetic radiation. The spectral mixing limits the accuracy of wheat planting area estimation. Meanwhile, endmember spectral variability also makes traditional spectral unmixing methods perform poorly. Thus, this study proposes a Self-supervised Learning-based Spectral Unmixing algorithm (SLSU) to alleviate the influence of spectral mixing and endmember spectral variability wheat planting area estimation. First, the variational autoencoder is used to achieve unsupervised interpretation of the endmember spectral variability and endmember library generation. Then, abundances corresponding to various endmembers are estimated by using the alternating least-squares strategy and fully constrained least squares. Finally, the unmixed results are corrected on the basis of the spatial neighborhood by using the probabilistic relaxation labeling algorithm to improve the accuracy of spectral unmixing and wheat extraction further. Three typical wheat planting areas in Xinxiang, Henan Province were selected as experimental areas, and the wheat planting area was obtained by Sentinel 2 images. The extraction accuracy was evaluated by wheat distribution data measured in the field, and the results showed that the median value and the R2 score of the wheat extraction’s relative extraction error are close to 1.3 pixels and 1.00, respectively, which are remarkably better than the results extracted by traditional spectral unmixing algorithms (including fully constrained least squares, extended linear mixed model and so on) and traditional supervised learning classification methods (such as support vector machines and random forests). The proposed SLSU algorithm can improve the accuracy and stability of wheat planting area estimation and provide an effective method for crop distribution extraction and planting area estimation.

mots-clés

remote sensing;self-supervised learning;variational auto encoder;spectral mixing;feature extraction;crop area estimation

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