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Title: Hyperspectral image classification using random forests and neural networks
Authors: Abe, B. T. 
Olugbara, Oludayo O. 
Marwala, T. 
Keywords: Generalized reduced gradient;Classifiers;Land cover classification;Hyperspectral image
Issue Date: 2012
Publisher: International Association of Engineers
Source: Abe, B.T.; Olugbara, O.O. and Marwala, T. 'Hyperspectral Image Classification using Random Forests and Neural Networks.' Proceedings of the World Congress on Engineering and Computer Science. 1(2012).
Spectral unmixing of hyperspectral images are
based on the knowledge of a set of unknown endmembers.
Unique characteristics of hyperspectral dataset enable different
processing problems to be resolved using robust mathematical
logic such as image classification. Consequently, pixel purity
index is used to find endmembers from Washington DC mall
hyperspectral image dataset. The generalized reduced gradient
algorithm is used to estimate fractional abundances in the
hyperspectral image dataset. The WEKA data mining tool is
selected to construct random forests and neural networks
classifiers from the set of fractional abundances. The
performances of these classifiers are experimentally compared
for hyperspectral data land cover classification. Results show
that random forests give better classification accuracy when
compared to neural networks. The study proffers solution to
the problem associated with land cover classification by
exploring generalized reduced gradient approach with learning
classifiers to improve overall classification accuracy. The
classification accuracy comparison of classifiers is important
for decision maker to consider tradeoffs in accuracy and
complexity of methods.
Appears in Collections:Research Publications (Accounting and Informatics)

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