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Proceedings Paper

Vegetation classification using hyperspectral remote sensing and singular spectrum analysis
Author(s): B. Hu; Qingmou Li
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Paper 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.

Paper Details

Date Published: 24 September 2007
PDF: 7 pages
Proc. SPIE 6696, Applications of Digital Image Processing XXX, 66960N (24 September 2007); doi: 10.1117/12.735278
Show Author Affiliations
B. Hu, York Univ. (Canada)
Qingmou Li, York Univ. (Canada)

Published in SPIE Proceedings Vol. 6696:
Applications of Digital Image Processing XXX
Andrew G. Tescher, Editor(s)

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