Table1. Similarity Measures Between The Original
Versus the Reconstructed Spectral Signature for one
Class in our image.
| Level |
Correlation |
| 1 |
.9974 |
| 2 |
.9936 |
| 3 |
.9804 |
| 4 |
.9558 |
| 5 |
.9224 |
Table2. Number of training data for classification
| Class Name |
Training data (NO.of pixels) |
| Wood |
1290 |
| Grass\paster |
467 |
| Soybeans-notill |
1108 |
| Corn |
700 |
| Corn-notill |
1527 |
| Hay_windrowed |
630 |
| Grass\trees |
868 |
| Alfalfa |
92 |
| Oatas |
303 |
| Grass\pastuer-moved |
2645 |
| Soybeans-clean |
710 |
| Corn-min |
921 |
4. Wavelet-Based Reduction and Classification Accuracy
We have experimentally validated our Wavelet Based dimension reduction by using remotely sensed image tests from a hyperspectral
scene. Using the ENvironment for Visualizing Images (ENVI) as a tool for classification accuracy to assess the accuracy of our
Wavelet Based method and PCA we calculated the error (confusion) matrix of several classification methods for the same level of
compression between the Wavelet and PCA.
Supervised classification methods are trained on labeled data. As the number of bands increases the number of training data for
classification is increased too. In usual the minimum number of training data for each class is 10N, where N is the number of bands.
The details about the number of training pixels are shown in Table2.
Now it is the turn of introducing four statistical supervised classification methods that we used in this work.
Maximum Likelihood (ML)
This method assumes that the statistical for each class in each band are normally distributed and computes all of the probability of
classes for each pixel and assign that pixel to the class with the highest probability value.
MahalaniBis distance (MB)
This algorithm is similar to ML algorithm but in MB algorithm it is assumed that the covariance matrix of all classes is equivalent and
of course this algorithm is faster than ML algorithm.
Minimum Distance (MD)
In this algorithm for each training sample region the mean is calculated and also the Euclidean distance between each pixel and the
means of each training sample region is calculated. The pixel is labeled to that class that has minimum distance.
ParallelePiped (PP)
The parallelepiped classifier is a very simple classifier, in this algorithm the range in all bands describes a multidimensional box or
parallelepiped, if, on classification, pixel are found to lie in such a parallelepiped they are labeled as belong to that class.