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Hyper Spectral Image Processing
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Adaptable Class Data Representation for Hyperspectral Image Classification
4. Discussion and Conclusion
Cluster-space data representation plays an important role in data analysis, especially for hyperspectral data. Firstly,
class distributions (spreadness) can be easily inspected. More importantly, the separability between the classes can
be examined and quantified. For example, 6.7% and 5.4% of the training pixels from class 2, ‘Corn’, and class 3,
‘Grass’, are classified into Cluster 2 (none from the rest of classes). While Cluster 2 will be assigned to class 2,
those samples can be selected for detailed examination in order to find out the physical reasons for overlapping.
There may be some bad samples which should be deleted from the reference data. Secondly, the probability
(density) values, H i(k), provides extra information on how reliable the classification results are. For example, if an
unknown pixel is labeled as Cluster 2 and therefore classified as ‘corn’, the assignment is not very reliable since
Cluster 2’s probability value is only 6.7%.
To reduce the number of overlapping clusters, the total number of clusters to generate needs to be sufficiently high.
However, the selection of the number of clusters to use requires experience or trial and error. If a large number of
clusters is used, it will increase the computational load.
The shapes of the density plots as shown in Fig. 2 are less important, since whatever the shape, it will be
represented directly by the discrete density values. If a shape which is closed to a normal distribution is preferred
for better presentation, it can be achieved by reordering the cluster index, assuming the class data is reasonably
separable.
The proposed classification method provides a means to represent data which are distributed other than normally.
The multiple signatures formed from the associated clusters are adaptable to the individual class distribution shape.
On the basis of experiments conducted so far, classification accuracy will be higher than that using minimum
distance classification. While the method is not as robust as the maximum likelihood method in terms of coping
with the noise in the data, the requirement for the training data size can normally be met more easily for
hyperspectral data.
5. Acknowledgement
The work presented in this paper was done in part when the author was a Visiting Fellow in the Department of
Forestry, The Australian National University and the author thanks Dr. B. Turner for his helpful discussions during
that time. The author also thanks Dr. D. Landgrebe of the School of Electrical and Computer Engineering,
Purdue University, for providing the AVIRIS data set and the MultiSpec software package.
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