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2 5 10 18 20 25 30
Number of Feature
Figure 4. Comparison of classification accuracies between WFE
methods
4.2 Experiment II: Comparison of Classification Results
between Wavelet-based Feature Extraction Methods
The purpose of the experiment II is to compare the performance
between wavelet-based feature extraction (WFE) methods. In
linear WFE, we use Daubechies 3 wavelet as basis function and
decompose signal from level 3 to level 5. The numbers of
features in different linear WFE methods are thirty, eighteen
and eleven respectively.
Figure 4 shows the classification accuracies between WFE
methods. First of all, the results of linear and nonlinear WFE
methods are similar, and the best accuracies of linear WFE
(level 3), linear WFE (level 4), linear WFE (level 5) and
nonlinear WFE are 8596, 86.67%, 89.33 and 83.33%. In
addition, the classification accuracies increase slightly in linear
WFE, when the level of decomposition increases.
4.3 Experiment III: Comparison of Classification Results
between Wavelet-Based and HHT-Based Feature
Extraction Methods
In experiment III, the purpose is to compare the performance
between wavelet-based and HHT-based methods. The
classification accuracies with different methods are showed in
Figure 5. First of all, the classification accuracies of WFE
methods and HHT-based methods are all conformed to Hughes
phenomenon that classification accuracy increases at first and
then declines as the number of feature grows.
Compared with the results of different methods, linear and
nonlinear WFE have similar classification results which have
been metioned in section 4.2. Also, the results of unsupervised
HHT-based method are similar to WFE methods but the
accuracies decrease obviously when the number of feature is
more than ten. Finally, supervised HHT-based feature
extraction can achieve better classification results than any
other methods.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
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5 10 15 20 25 30
Number of Feature
Figure 5. Comparison of classification accuracies between
wavelet-based and HHT-based feature extraction methods
5. CONCLUSION
In this study, two feature extraction methods using Hilbert-
Huang transform were proposed to extract useful features for
hyperspectral image classification. The results of HHT-based
methods are compared with wavelet-based feature extraction
methods.
According the experiments, the results of unsupervised HHT-
based methods are similar to the result of WFE which is
implemented in this study, but the accuracies of unsupervised
HHT-based method are unstable when the feature increases.
Subsequently, when computing the separability of different
classes with training samples, supervised HHT-based method
can have better result than unsupervised HHT-based method
and can reach 90% classification accuracy with six or seven
features. Furthermore, it also has superior classification
accuracies than linear and nonlinear WFE. By extracting
features from Hilbert spectrum, we can not only reduce the
dimensionality of hyperspectral image but also get a small
number of salient features for classification. Therefore, Hilbert-
Huang is an appropriate and effective tool for hyperspectral
image analysis.
In the future, the effectiveness of HHT-based methods still
could be improved. In addition, the objects in the experiments
are mainly the minerals. It is another object to investigate that
HHT-based feature extraction methods proposed in this study
are suitable and have similar/better results than WFE methods
for other kind of material objects such as metropolitan area of
vegetation area.
REFERENCES
AVIRIS Airborne Visible/Infrared Imaging Spectrometer, 2011.
AVIRIS Data - Ordering Free AVIRIS Standard Data Products.
http://aviris.jpl.nasa.gov/data/free data.html (14 Jul. 2011).
Bellman, R., 1961. Adaptive Control Processes: A Guided Tour,
Princeton University Press.
Fukunaga, K., 1990. Introduction to Statistical Pattern
Recognition, Second edition, San Diego: Academic Press, Inc.