Study on Vegetation Classification Based on Spectral Knowledge Base

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.
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Peng Liu, Jingcheng Zhang, Bin Wang, Xuexue Zhang, Kaihua Wu. Study on Vegetation Classification Based on Spectral Knowledge Base. 11th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Aug 2017, Jilin, China. pp.310-320, ⟨10.1007/978-3-030-06179-1_32⟩. ⟨hal-02111521⟩

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