The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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has been evaluated for discrimination of vegetation area from 
others. Atmospheric correction and other pre-processing 
operations on the hyperspectral imagery have been performed 
by using routine procedures. 
Spectral unmixing algorithms use a variety of different 
mathematical procedures to endmember extraction and estimate 
abundances. Unmixing problem comprises three sequential 
steps: dimension reduction, endmember determination, and 
inversion. Because hyperspectral scenes can include extremely 
large amount of data, some algorithms for spectral unmixing 
first use image itself to estimate endmembers present in the 
scene. The dimension-reduction stage reduces the dimension of 
the original data in the scene. This step is optional and is 
invoked only by some algorithms to reduce the computational 
cost of subsequent processing; it also selects bands with higher 
signal to noise ratio (SNR) to separate endmembers spectra 
without any data loss. We use the MNF method to achieve this 
goal. In this paper, we introduce that the site study and EEA that 
applied on image then emphasize on the second step of 
unmixing problem. Finally represent the result of this research. 
1.2. THE SITE STUDY 
An agricultural area located in southern parts of Tehran, known 
as Ahmadabad has been selected as the study site. Wheat and 
barley are the main agricultural crops in the area. More than 30 
fields of detailed ground-truth dataset have been visited in the 
field and their records have been used as a ground truth data for 
training and verifying the results of the classification. 
1.3. OPTIMAL BANDS IN HYPERION IMAGES 
Hyperion data were acquired over the Ahmadabad village on 
May 21, 2002 at 06:57:56 GMT. The EO-1 satellite is a sun- 
synchronous orbit at 705 km altitude. Hyperion data includes 
256 pixels with a nominal size of 30 m on the ground over a 
7.65 km swath. Well-calibrated data (Level 1B1) is normally 
available. Post-Level 1B1 processing of the dataset, as 
performed in this study, included correction for bad lines, 
striping pixels and smile, atmospheric correction and co 
alignment. Hyperion data is acquired in pushbroom mode with 
two spectrometers. One operates in the VNIR range (including 
70 bands between 356-1058 nm with an average FWHM of 
10.90 nm) and the other in the SWIR range (including 172 
bands between 852- 2577nm, with an average FWHM of 10.14 
nm). 44 of 242 bands including bands 1-7, 58-76 and 225-242 
are set to zero by TRW software during Level 1B1 processing 
[ 2 ][ 3 ]. Post-level 1B1 data processing operations for preparation 
of the Hyperion data for classification including band selection, 
correction for bad lines, striping pixels and smile, a pixel-based 
atmospheric correction using FLAASH [2] and a co-alignment 
were performed. 
2. IDENTIFY OPTIMAL ENDMEMBER 
An important problem in Hyperspectral image processing is to 
decompose the mixed pixels into the materials that contribute to 
the pixel, endmember and a set of corresponding fractions of the 
spectral signature in the pixel, abundances, and this problem is 
known as the unmixing problem. 
According to the definition, an endmember is an idealized pure 
signature of a class. Endmember extraction is one of the 
fundamental and crucial tasks in hyperspectral data exploitation. 
It has received considerable interest in recent years, with many 
researchers devoting their effort to develop algorithms for 
endmember extraction from hyperspectral data. 
Hyperspectral imaging sensors on environmental applications 
have high spectral resolution and low spatial resolution so that 
numerous disparate substances can contribute to the spectrum 
measured from a single pixel or in the field of view of the 
sensor. A mixed pixel occurs due to high spectral resolution or 
low spatial resolution when more than one material substance 
present in a pixel, in this case these substances are considered to 
be mixed linearly or nonlinearly in the pixel. 
Recent researches aim to identify the individual constituent 
materials existing in the mixture pixel, as well as the 
proportions in which they appear. Spectral unmixing is the 
procedure by which the measured spectrum of a mixed pixel is 
decomposed into a collection of constituent object spectra that 
present in scene. In estimating the constituent members and 
abundances in pixel spectra, unmixing algorithms incorporate 
philosophical assumptions regarding the physical mechanisms 
and mathematical structure by which the reflectance properties 
from disparate substances combine to yield the mixed pixel 
spectra. Detection algorithms make similar assumptions when 
testing for the existence of a specific substance in a mixed pixel. 
Not surprisingly, the hyperspectral detection and unmixing 
problems are closely related. 
2.1. PPI 
The Pixel Purity Index (PPI) has been widely used in 
hyperspectral image analysis for endmember extraction due to 
its publicity and availability in the Environment for Visualizing 
Images (ENVI) software.[4] In this experiment, the PPI was 
implemented to find endmembers for the image scene in Fig 1, 
using the same set of randomly generated initial skewers. Fig 2 
shows the endmember pixels extracted by the PPI, respectively. 
Fig 1 Fig 2 
Fig 1 :Hyperio image scene subset 
(R=650.67,G=569.27,B=477.69) 
Fig 2 :Hyperion PPI 
2.2. SMACC 1 
A new endmember extraction method has been developed that 
is based on a convex cone model for representing vector data. 
The endmembers are selected directly from the data set. The 
algorithm for finding the endmembers is sequential: the convex 
cone model starts with a single endmember and increases 
incrementally in dimension. Abundance maps are 
simultaneously generated and updated at each step. A new 
endmember is identified based on the angle that it makes with 
the existing cone. The data vector which is making the 
maximum angle with the existing cone is chosen as the next 
- Sequential Maximum Angle Convex Cone