24 September 2007 Vegetation classification using hyperspectral remote sensing and singular spectrum analysis
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Proceedings Volume 6696, Applications of Digital Image Processing XXX; 66960N (2007); doi: 10.1117/12.735278
Event: Optical Engineering + Applications, 2007, San Diego, California, United States
Abstract
In this study, classification was investigated based on seasonal variation of the state parameters of vegetation canopies as inferred from visible and near-infrared spectral bands. This analysis was carried out on data collected over agricultural fields with the hyperspectral CHRIS (Compact High Resolution Imaging Spectrometer) in May, June and July, 2004. The singular spectrum analysis was used to remove noise in each reflectance spectrum of the whole image. Decision tree classification was performed on different features, such as reflectance, vegetation indices, and principal components acquired by PCA (Principal Component Analysis) and MNF (Minimum Noise Fraction). The results demonstrated noise-removal using SSA increased classification accuracy by 3-6 percentages depending on the features used. Classification using MNF components was shown to provide the highest accuracy followed by that using vegetation indices.
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B. Hu, Qingmou Li, "Vegetation classification using hyperspectral remote sensing and singular spectrum analysis", Proc. SPIE 6696, Applications of Digital Image Processing XXX, 66960N (24 September 2007); doi: 10.1117/12.735278; https://doi.org/10.1117/12.735278
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KEYWORDS
Reflectivity

Vegetation

Principal component analysis

Remote sensing

Spectrum analysis

Agriculture

Data acquisition

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