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VEGETATION ENDMEMBER EXTRACTION IN HYPERION IMAGES 
M. Heidari Mozaffar a , M.J. Valadan Zoej b , M.R. Sahebi c , Y. Rezaei d 
a Remote Sensing Department, KNToosi University of Technology, Mirdamad Cross, Valiasr Av., Tehran, Iran, 
Morteza_Heidarymzaffar@yahoo.com 
b Remote Sensing Department, KNToosi University of Technology, valadanzouj@kntu.ac.ir 
c Remote Sensing Department, KNToosi University of Technology, sahebi@kntu.ac.ir 
d Remote Sensing Department, KNToosi University of Technology, y.rezaei@gmail.com 
Commission WG VII/3 
KEY WORDS: Hyperspectral imaging, Endmember Extraction, Hyperion Images 
ABSTRACT: 
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. An 
important problem in Hyperspectral imaging 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. An ultimate 
goal of an Endmember Extraction Algorithm (EEA) is to find the purest form of each spectrally distinct material on a scene. 
Endmember extraction tendency to the type of endmembers being derived, and the number of endmembers, estimated by an 
algorithm, with respect to the number of spectral bands, and the number of pixels being processed, also the required input data, and 
the kind of noise, if any, in the signal model surveying done. Identifying endmembers that satisfy both physical and mathematical 
imperatives is a considerable challenge, making autonomous endmember determination the hardest part of the unmixing problem. Of 
three stages that comprise unmixing, endmember determination is the most closely aligned with the material identification 
capabilities of unmixing. Non-statistical algorithms or Geometrical approach essentially assume the endmembers are deterministic 
quantities, whereas statistical approaches view endmembers as either deterministic, with an associated degree of uncertainty, or as 
fully stochastic, with random variables having probability density functions. In addition, specific features concerning the outputs, 
inputs, and noise models used by these algorithms are included according to the model specifically distinguishing the properties of 
endmember-determination algorithms. 
In this paper, Endmember Extraction Algorithms (EEAs) applied on a Hyperion image of southern of Tehran, IRAN. A large number 
of endmembers were suggested to enhance the classification accuracy while the seasonal variation in the spectral response should be 
taken into account in vegetation classification. We compare results of Geometrical approach in vegetation endmember extraction 
assistance with vegetation indices. 
1. INTRODUCTION 
1.1. OVERVIEW 
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. An important problem in Hyperspectral imaging 
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. 
Availability of new generation of hyperspectral sensors such as 
the Hyperion has lead to the new challenges in the area of crop 
type mapping and agricultural management. The sensor’s 242 
Spectral band between 400 and 2500 nm (level 1B1 ) and spatial 
resolution of 30m bear high potentials for agricultural crop 
discrimination and detailed land use classification[ 1 ]. 
Advantages of this technology include both the qualitative 
benefits derived from a visual overview, and more importantly, 
the quantitative abilities for systematic assessment and 
monitoring. In every remotely sensed image, a considerable 
number of mixed pixels is present. A mixed pixel is a picture 
element representing an area occupied by more than one ground 
cover type.Several research objectives were accomplished: 
(1) Select optimal bands in hyperspectral images those are most 
useful in vegetation classification, 
(2) Identify optimal endmember, signature spectrum that 
represents a certain class, for vegetation classification, and 
(3) Test effective Endmember Extraction Algorithms for 
classification of vegetation type. 
First pre-processing step which iclude radiometric correction 
and removing atmospheric effect involves calibration and 
atmospheric correction. We used Fast Line-of-sight 
Atmospheric Analysis of Spectral Hypercube (FLAASH) in 
ENVI software for atmospheric correction. In this research, 
capabilities of Hyperion hyperspectral imagery acquired from 
an agricultural area located in southern parts of Tehran, Iran ,