DocumentCode :
411039
Title :
Soil texture classification using wavelet transform and maximum likelihood approach
Author :
Zhang, Xudong ; Younan, N.H. ; King, R.L.
Author_Institution :
Dept. of Electr. & Comput. Eng., Mississippi State Univ., MS, USA
Volume :
4
fYear :
2003
fDate :
21-25 July 2003
Firstpage :
2888
Abstract :
In this paper, a wavelet-based soil texture classification system is proposed for identifying soil with different textures. The wavelet transform is used for feature extraction. The wavelet is a systematic and powerful tool for signal and image analysis due to its multiresolution characteristic. The maximum likelihood (ML) classifier is designed using a set of training samples. The ML parameter estimation method has been shown to give out optimal results. During the process of training and classification, the Fisher´s Linear Discrimination Analysis (FLDA) is incorporated for feature vector dimension reduction and optimization. Three different soil texture images, i.e., sand, silt, and clay are used for training and classification. Experimental results and discussion are presented.
Keywords :
feature extraction; image classification; image resolution; image texture; soil; terrain mapping; wavelet transforms; Fisher linear discrimination analysis; feature extraction; image analysis; maximum likelihood classifier; multiresolution; parameter estimation; signal analysis; soil texture images; wavelet transform; wavelet-based soil texture classification system; Feature extraction; Image resolution; Image texture analysis; Maximum likelihood estimation; Parameter estimation; Signal resolution; Soil texture; Vectors; Wavelet analysis; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International
Print_ISBN :
0-7803-7929-2
Type :
conf
DOI :
10.1109/IGARSS.2003.1294621
Filename :
1294621
Link To Document :
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