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Study on Vegetation Classification Based on Spectral Knowledge Base

  • Peng Liu
  • Jingcheng ZhangEmail author
  • Bin Wang
  • Xuexue Zhang
  • Kaihua Wu
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)

Abstract

A framework about spectral based vegetation classification was proposed, which serves as a core methodology of the vegetation spectral knowledge base. The hyperspectral reflectances of 13 types of plants were measured by an ASD FieldSpec 4 spectroradiometer. Two forms of spectral features were used for representing the key spectral characteristics of plants, including Vegetation index (VI) and spectral shape features. Based on these spectral features, a sensitivity analysis was performed to identify the most important features for establishing the classifier. The analysis of variance (ANOVA) and the cross-correlation analysis were applied to derive the sensitivity of features and remove features that have high correlations. Then, a classification method for differentiating plants was established by coupling some spectral similarity measures (e.g., ED) with some classification methods (e.g., BPANN and SVM). The results of discrimination analysis showed that a highest accuracy was produced by SVM with the OAA over 99% when using 7 sensitive VIs. The results suggested the framework about spectral based vegetation classification can form a basis for spectral knowledge base and application technology and further achieve a wide range of plant classification based on remote sensing.

Keywords

Vegetation classification Hyperspectral Feature extraction Classification algorithm 

Notes

Acknowledgements

This work was supported by Zhejiang public welfare programme of agriculture technology (2016C32087), National Natural Science Foundation of China (41671415; 41601461) and Graduate Scientific Research Foundation of Hangzhou Dianzi University (CXJJ2017068).

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Peng Liu
    • 1
  • Jingcheng Zhang
    • 1
    Email author
  • Bin Wang
    • 1
  • Xuexue Zhang
    • 1
  • Kaihua Wu
    • 1
  1. 1.College of Life Information Science and Instrument EngineeringHangzhou Dianzi UniversityHangzhouChina

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