期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 43, 期 3, 页码 615-618出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2004.841476
关键词
Hidden Markov models (HMMs); hyperspectral signals; maximum-likelihood (ML) classification; soil texture; wavelets
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.
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