The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 200H
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Feature extracted Methods
Miss classification (%)
All bands
8.77
PCA
9.04
LDA
8.05
Ploy-KPCA d = l,p = 0
11.84
RBF-KPCA <j 2 = 10 7
16.81
Ploy-GDA d = 2,p = 0
7.46
RBF-GDA cr 2 =10 7
1.46
RBF-GDA cr 2 =10 8
3.51
Table 3. The precision of classification with features extracted
with different methods.
We evaluated the classification precision with the testing
samples, using the minimum distance classifier. The
classification result with the feature extracted by RBF-GDA
was shown in Figure 6. The classification result of different
feature extraction methods is shown in Table 3.
4. CONCLUSION
Through the experiments of feature extraction with AVIRIS and
PHI images we made some conclusions.
The PCA is to find project directions, which can make the
samples variance maximized. The KPCA, using the kernel
function, can realize the information compression to a great
extent, but it is not good for classification.
When the kernel function and its parameters are correctly
selected, in the feature space extracted by GDA, the samples of
the same class are near with each other; the samples of the
different classes are far away. The GDA is a feature extracting
method which is more suitable to classification than the LDA.
When the kernel function and its parameters are correctly
selected, the classification precision is much better with the
features extracted by GDA, than the features extracted by other
methods. How to select the kernel function and find suitable
parameter is our further research.
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