Wavelet domain statistical hyperspectral soil texture classification

This communication presents an automatic soil texture classification system using hyperspectral soil signatures and wavelet-based statistical models. Previous soil texture classification systems are closely related to texture classification methods, where images are used for training and testing. In...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2005-03, Vol.43 (3), p.615-618
Main Authors: Xudong Zhang, Younan, N.H., O'Hara, C.G.
Format: Article
Language:English
Subjects:
Classification
Classification algorithms
Hidden Markov models
Hidden Markov models (HMMs)
Hyperspectral imaging
Hyperspectral sensors
hyperspectral signals
Mathematical models
maximum-likelihood (ML) classification
Reflectivity
Robustness
Soil (material)
Soil texture
Statistical analysis
Surface layer
Surface structures
System testing
Tasks
Texture
Wavelet domain
wavelets
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Description
Summary:This communication presents an automatic soil texture classification system using hyperspectral soil signatures and wavelet-based statistical models. Previous soil texture classification systems are closely related to texture classification methods, where images are used for training and testing. In this study, we develop a novel system using hyperspectral soil textures, which provide rich information and intrinsic properties about soil textures, where two wavelet-domain statistical models, namely, the maximum-likelihood and hidden Markov models, are incorporated for the classification task. Experimental results show that these methods are both reliable and robust.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2004.841476