Identification and Extraction of Nutrient Content in Hyperspectral Black Soil in Frequency Domain

  • Dong-hui ZhangEmail author
  • Ying-jun Zhao
  • Kai Qin
  • Dong-hua Lu
  • Cheng-kai Pei
  • Ning-bo Zhao
  • Yue-chao Yang
  • Ming Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 657)


Hyperspectral remote sensing technology, with its high spectral resolution and high spatial resolution, plays a more and more important role in the quantitative remote sensing monitoring of black soil. In order to extract the information from the current spectrum and the object-oriented method, it is impossible to integrate the spectral domain and the space domain to explore the feasibility of the frequency domain processing method to improve the recognition accuracy. The CASI/SASI aero hyperspectral data are obtained from the Jiansanjiang area of Northeast China, and 60 samples are collected on the ground, and the content of organic matter is tested. The characteristics of the amplitude spectrum and phase spectrum of the typical black land are studied, and an adaptive classifier based on Gauss filter is designed for the hyper spectral space spectrum analysis algorithm, and an air spectrum classification framework based on the optimization of the ground laboratory data is constructed. Compared with the traditional hyperspectral classification algorithm, the frequency domain recognition and extraction algorithm proposed in this paper have described and characterized the hyperspectral data from a new viewpoint, which solve the uncertainty of hyperspectral data. In the future, this method may be a new thought to improve the traditional data processing method.


Black soil nutrient Frequency domain characteristics Adaptive gauss low-pass filter Aero hyperspectral Hyperspectral remote sensing 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Dong-hui Zhang
    • 1
    Email author
  • Ying-jun Zhao
    • 1
  • Kai Qin
    • 1
  • Dong-hua Lu
    • 1
  • Cheng-kai Pei
    • 1
  • Ning-bo Zhao
    • 1
  • Yue-chao Yang
    • 1
  • Ming Li
    • 1
  1. 1.National Key Laboratory of Remote Sensing Information and Imagery Analyzing TechnologyBeijing Research Institute of Uranium GeologyBeijingChina

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