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Wavelet domain statistical hyperspectral soil texture classification
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DOI：10.1109/TGRS.2004.841476.png)
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摘要
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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.
Keyword:
Hidden Markov models (HMMs)
hyperspectral signals
maximum-likelihood (ML) classification
soil texture
wavelets
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期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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