Leaf area index estimation with EnMAP hyperspectral data based on deep neural network
投稿时间:2019-03-23  修订日期:2019-12-18  download
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李雪玲 首都师范大学 数学科学学院北京 100048 xueling_li@cnu.edu.cn 
董莹莹 中国科学院空天信息创新研究院数字地球重点实验室北京 100094
朱溢佞 首都师范大学 数学科学学院北京 100048  
黄文江 中国科学院空天信息创新研究院数字地球重点实验室北京 100094
Abstract:Regional leaf area index (LAI) mapping is important for crop growth monitoring and yield estimation. Due to the lower accuracy and instability of statistical models for regional LAI estimation, we proposed a new deep neural network model, i.e. Small Simple Learning LAI-Net (SSLLAI-Net), based on small sample training, to achieve stable relationship between hyperspectral reflectance and LAI. The new proposed SSLLAI-Net was constructed with two convolution layers, one pooling layer and three connect layers, for which the inputs and outputs were hyperspectral reflectance and LAI estimation. Moreover, SSLLAI-Net could support small training sets. We applied SSLLAI-Net to an Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery for regional LAI mapping, in which cereals, maize, rape seed and other crops are selected as our objects. The achieved R2 values for estimated LAI of cereals, maize, rape seed and other crops were 0.95, 0.99, 0.98 and 0.90 based on small training sets with 50 samples, while for the inputs with noise, the R2 values were 0.95、0.98、0.96 and 0.89, respectively. In all, our new proposed SSLLAI-Net has high precision of regional LAI mapping, stability and noise resistance with hyperspectral remote sensing observations.
keywords:leaf area index (LAI)  hyperspectral remote sensing  EnMAP  deep neural network  SSLLAI-Net
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Copyright:《Journal of Infrared And Millimeter Waves》