DocumentCode :
2937537
Title :
Random forests of binary hierarchical classifiers for analysis of hyperspectral data
Author :
Crawford, Melba M. ; Ham, Jisoo ; Chen, Yangchi ; Ghosh, Joydeep
Author_Institution :
Center for Space Res., Austin, TX, USA
fYear :
2003
fDate :
27-28 Oct. 2003
Firstpage :
337
Lastpage :
345
Abstract :
Statistical classification of hyperspectral data is challenging because the input space is high in dimension and correlated, but labeled information to characterize the class distributions is typically sparse. The resulting classifiers are often unstable and have poor generalization. A new approach that is based on the concept of random forests of classifiers and implemented within a multiclassifier system arranged as a binary hierarchy is proposed. The primary goal is to achieve improved generalization of the classifier in analysis of hyperspectral data, particularly when the quantity of training data is limited. The new classifier incorporates bagging of training samples and adaptive random subspace feature selection with the binary hierarchical classifier (BHC), such that the number of features that is selected at each node of the tree is dependent on the quantity of associated training data. Classification results from experiments on data acquired by the Hyperion sensor on the NASA EO-1 satellite over the Okavango Delta of Botswana are superior to those from our original best basis BHC algorithm, a random subspace extension of the BHC, and a random forest implementation using the CART classifier.
Keywords :
data analysis; feature extraction; image classification; spectral analysis; statistical distributions; trees (mathematics); Botswana; CART classifier; Hyperion sensor; NASA EO-1 satellite; Okavango Delta; adaptive random subspace feature selection; associated training data; binary hierarchical classifier algorithm; binary hierarchy; class distributions; generalization; hyperspectral data analysis; multiclassifier system; random forests; statistical classification; trees; Classification tree analysis; Covariance matrix; Data analysis; Feature extraction; Hyperspectral imaging; Hyperspectral sensors; Instruments; NASA; Satellites; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Techniques for Analysis of Remotely Sensed Data, 2003 IEEE Workshop on
Print_ISBN :
0-7803-8350-8
Type :
conf
DOI :
10.1109/WARSD.2003.1295213
Filename :
1295213
Link To Document :
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