Abstract
This paper describes different approaches in feature extraction for a hyper spectral image classification. For the actual feature extraction, principal components transformations, band correlation method, average intensity of the visible/infrared ranges and spectral knowledge are used. The output of each of the feature extraction method is used for a classification process. The results are analyzed and compared.

Introduction
Extraction of a reliable feature and improvement in a classification accuracy have been one of the main tasks of many researchers dealing with a digital image processing. Over the years, many techniques have been developed and tested for processing and analysis of convectional multispectral data with fewer dimensionality. For such data, feature extraction can be easily done via either less correlated bands or transforming the actual dataset into fewer reliable features. In recent years, processing of hyper spectral data has attracted many researchers dealing with RS image processing. Unlike the traditional multispectral data taken in the optimal range of electro-magnetic spectrum, the hyper spectral data deals with a great number of bands and many attempts are being made to reduce the dimensionality of the data and extract reliable information needed for various decision making processes [1,2,5,6]. Feature extraction for hyper spectral data is a time consuming process because it requires extensive search time until the reliable features are found. The aim of this paper is to apply different methods for feature extraction and selection of the reliable bands in classification of hyper spectral images using commercial software. For this purpose, airborne AVIRIS dataset is used. In the final classification, a traditional method of a maximum likelihood classifier (MLC) and compared. The analyses are carried out using ENVI-system initialed in a Sun-sparc workstation.

Feature Extraction Methods
For the feature extraction the following approaches have been used:

Feature extraction using principal component transformation (PCT). Here, two different approaches are used. The first method transforms the overall dataset into orthogonal axes, whereas the second method, first splits the overall dataset into groups each of which contains highly correlated bands and then transforms each dataset falling into the defined group into the principle axes.

Define a correlation matrix and select the features according to the lower correlation among the bands and cluster separability in multi-dimensional feature space.

Define average intensity of each range (ie, blue, green, red, NIR and MIR) and use each one as a separate feature and compress the overall range using the PCA technique.

Application of spectral knowledge of the classes of interest. The spectral knowledge is defined on the basis of the general spectral characteristics of the classes of objects and the available spectral library.

The Study Area and Data Sources

The study area was selected in Jasper Ridge, Canada. The selected classes were settlement, gravel tin shed, deciduous forest, irrigated vegetation and soil. There was a high spectral mixture among the classes. The original AVIRIS dataset was reduced from 224 bands to 198 after water absorption bands and the bands with totally zero values were excluded.

Analysis and Discussion

The training sample selection
The training samples representing the selected classes have been selected through thorough analysis. For the selection of the training samples, two different approaches have been used:

1. using polygons covering the representative pixels of the selected classes. Here, the pixels with varying radiometric values are covered by a polygon although they represent the same class;
2. selecting pixels by one by one from different areas thus selecting only the representative pixels with highest purity.

Before the actual classification, the samples were analyzed by pixel purity index and n-dimensional visualiser.