NDLI: Brushlet transform for hyperspectral feature extraction in automated detection of nutsedge presence in soybean

Content Provider	IEEE Xplore Digital Library
Author	Yan Huang Bruce, L.M. Jiang Li Leon, C. Shaw, D.
Copyright Year	2001
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Mississippi State Univ., MS, USA (Yan Huang)
Abstract	To detect any differences in the leaf reflectance of weed-free soybean versus soybean in the presence of purple nutsedge, a brushlet-based automated classification system is presented in this paper. For a given hyperspectral reflectance curve, the automated system completes the following: the brushlet transform is computed; energy feature vectors are extracted from the brushlet coefficients; Fisher's linear discriminant analysis is used to reduce the dimensionality of the feature vector; and a minimum distance classifier is used to assign the curve to its appropriate class. Cross-validation testing is used to evaluate the classification performance. The experimental results show that with the use of the brushlet transform, the classification accuracy can be increased by as much as 38% when compared to more traditional data reduction methods, such as principal component analysis.
Starting Page	527
Ending Page	529
File Size	680514
Page Count	3
File Format	PDF
ISBN	0780370317
DOI	10.1109/IGARSS.2001.976211
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Hyperspectral imaging Frequency Signal resolution Fourier transforms Reflectivity Fast Fourier transforms Tiles Data mining Soil
Content Type	Text
Resource Type	Article

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