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Wavelet-domain hyperspectral soil te...

Zhang, Xudong.

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Wavelet-domain hyperspectral soil texture classification.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Wavelet-domain hyperspectral soil texture classification./
作者:	Zhang, Xudong.
面頁冊數:	70 p.
附註:	Source: Masters Abstracts International, Volume: 42-05, page: 1835.
Contained By:	Masters Abstracts International42-05.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1418828
ISBN:	0496236016

Wavelet-domain hyperspectral soil texture classification.
Zhang, Xudong.

Wavelet-domain hyperspectral soil texture classification. - 70 p.

Source: Masters Abstracts International, Volume: 42-05, page: 1835.

Thesis (M.S.)--Mississippi State University, 2004.

This thesis presents an automatic soil texture classification system using hyperspectral soil signals and wavelet-based statistical models. Previous soil texture classification systems are closely related to texture classification methods, which use images for training and testing. Although using image-based algorithms is a straightforward way to conduct soil texture classification, our research shows that it does not provide reliable and consistent results. Rather, we develop a novel system using hyperspectral soil textures, better known as hyperspectral soil signals, which provide rich information and intrinsic properties about soil textures. Hyperspectral soil textures, in their very nature, are nonstationary and time-varying. Therefore, the wavelet transform, which is proven to be successful in such applications, is incorporated. In this study, we incorporate two wavelet-domain statistical models, namely, the maximum likelihood (ML) and the hidden Markov model (HMM) for the classification task. Experimental results show that this method is reliable and robust. It is also more effective and efficient in terms of practical implementation than the traditional image-based methods.

ISBN: 0496236016Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Wavelet-domain hyperspectral soil texture classification.
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502 $a Thesis (M.S.)--Mississippi State University, 2004.
520 $a This thesis presents an automatic soil texture classification system using hyperspectral soil signals and wavelet-based statistical models. Previous soil texture classification systems are closely related to texture classification methods, which use images for training and testing. Although using image-based algorithms is a straightforward way to conduct soil texture classification, our research shows that it does not provide reliable and consistent results. Rather, we develop a novel system using hyperspectral soil textures, better known as hyperspectral soil signals, which provide rich information and intrinsic properties about soil textures. Hyperspectral soil textures, in their very nature, are nonstationary and time-varying. Therefore, the wavelet transform, which is proven to be successful in such applications, is incorporated. In this study, we incorporate two wavelet-domain statistical models, namely, the maximum likelihood (ML) and the hidden Markov model (HMM) for the classification task. Experimental results show that this method is reliable and robust. It is also more effective and efficient in terms of practical implementation than the traditional image-based methods.
590 $a School code: 0132.
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