An efficient methodology to simulate mixed spectral signatures of land covers, from endmember data, using linear statistical modelling based on the least squares estimation approach, is proposed. The optimal set of endmember has been obtained by measurements in situ with a field spectroradiometer GER 1500. Also, it is proposed the use of new sub-pixel methods based on statistics and certain “units of sampling” to apply to the landscapes. The resultant point estimations for these new units will be the “observations” and all of them will carry out an special role to simulate the final spectral signature. This methodology is used to simulate spectral signatures of a Mediterranean forest landscape near to Madrid (Spain). Furthermore the spectral signature model obtained through Field Radiometry data will be correlated with the image data of the same zone provided by the Landsat 7 Enhaced Thematic Mapper Plus (ETM+) sensor once corrected. The results obtained in correlation studies seem to conclude its efficiency. At the same time, the results open new research guidelines

Arquero Hidalgo, Águeda

Martínez Izquierdo, María Estíbaliz

Vázquez Sierra, José Manuel

English

Servicio de Coordinación de Bibliotecas de la Universidad Politécnica de Madrid

AN EFFICIENT METHODOLOGY TO SIMULATE MIXED SPECTRAL SIGNATURES OF 
LAND COVERS THROUGH FIELD RADIOMETRY DATA 
 
The mapping of land cover and land use is a key application of remotely sensed data. A thematic map 
shows the spatial distribution of identifiable earth surface features; it provides an informational 
description oven a given area, rather than a data description. Image classification is the process used to 
produce thematic maps from imagery.  
  
Traditionally, the production of maps from satellite imagery assumes that each pixel of the 
image can be assigned to a single land cover class. In the remote sensing imagery, the 
measured spectral radiance of a pixel is the integration of the radiance reflected from all the 
objects within the Ground Instantaneous Field Of View, GIFOV, also called Ground Sample 
Distance, GSD (Schowengerdt 2007). Mixed pixels are generated if the size of the pixel 
includes more than one type of land cover. Obviously, spectral mixing is inherent in any 
finite-resolution digital imagery of a heterogeneous surface. Solving the spectral mixture 
problem is, therefore involved in image classification, referring to the technique of spectral 
unmixing. 
  
Spectral unmixing has been used as a technique for analyzing the mixture of components in remotely 
sensed images. The technique is based on the assumptions that several primitive classes of interest can 
be selected, that each of these primitive classes has a pure spectral signature, which can be identified 
and the mixing between these classes can be adequately modelled as a linear combination (Small 
2004) of the spectral signatures.  
  
Spectral mixture analysis (SMA) techniques have overcome some of the weaknesses of full pixel 
approaches by using linear statistical modelling and signal processing techniques (Keshava 2002) 
(Rand 2001) (Tu 1999). The key task in linear SMA is to find an appropriate set of pure spectral 
components which are then used to estimate the fractional abundances of each mixed pixel from its 
spectrum and the endmember spectra by using a linear mixture model (Heinz 2000). The identification 
of the pure pixel value is often difficult. Because of sensor noise and within-class signature variability, 
endmembers only exist as a conceptual convenience and as idealization in real images.  
 
In this investigation, it is proposed a methodology to simulate mixed spectral signatures of land 
covers, from endmember data obtained through Field Radiometry, using linear statistical modelling. 
Previously, the authors have experience to collect a optimal set of endmembers by measurements in 
situ with a field spectroradiometer (Vazquez 2004, 2008). They propose the use of new sub-pixel 
methods based on statistics and certain “units of sampling” to apply to the landscapes. The resultant 
point estimations for these new units will be the “observations” that will be crucial later to simulate 
signature spectral models. Good results about correlation remote and near spectral response have been 
obtained. 
 
- SCHOWENGERDT, R. A. (2007) Remote Sensing. Models and Methods for Image Processing. Third ed. USA. 
Academic Press (Elsevier).  
- SMALL C. (2004) The Landsat ETM+ spectral mixing space. Remote Sensing of Environment, 93, 1 –17.  
- KESHAVA, N. and MUSTARD J.F., (2002) Spectral unmixing, IEEE Signal Process. Mag. 19  44–57.  
- RAND R.S. and KEENAN D.M., (2001) A spectral mixture process conditioned by Gibbs-based partitioning, IEEE 
Trans. Geosci. Remote Sens. 39, 1421–1434.  
- TU, T.M., SHYU, H.C., LEE, C.H., and CHANG C.I., (1999) An oblique subspace projection approach for mixed pixel 
classification in hyperspectral images, Pattern Recognition, 32, 1399–1408.  
- HEINZ D. and CHANG C.I., (2000) Fully constrained least squares linear mixture analysis for material quantification in 
hyperspectral Imagery, IEEE Trans. Geosci. Remote Sens. 39, 529–545. 
- VAZQUEZ, J. M., ARQUERO, A. and MARTINEZ, E. (2004) An efficient methodology for endmembers selection by 
field radiometry: an application to multispectral mixture model. Signal and Image Processing for Remote Sensing X, 
5573, 435-448. 
- VAZQUEZ, J.M., ARQUERO, A. and MARTINEZ, E. (2008) Aplicación de modelos estadísticos a la selección  
eficiente de cubiertas tipo mediante Radiometría de Campo. IEEE Latin America Transactions, In press. 
 
 
 
AN EFFICIENT METHODOLOGY TO SIMULATE MIXED SPECTRAL 
SIGNATURES OF LAND COVERS THROUGH FIELD RADIOMETRY DATA
VAZQUEZ SIERRA, J. M.1, MARTINEZ IZQUIERDO, E.2 & ARQUERO HIDALGO, A.2
1Dept. of Electr. and  Telec. Engineering, Politech. Sch., CEU University – Saint Paul, Madrid, SPAIN
jmvazquez.eps@ceu.es
2Dept. of Arq. and Tech. of Inform. Systems, FI, Politech. University of Madrid, Madrid, SPAIN
emartinez@fi.upm.es, aarquero@fi.upm.es
ABSTRACT
An efficient methodology to simulate mixed spectral signatures of land covers, from endmember data, 
using linear statistical modelling based on the least squares estimation approach, is proposed. The 
optimal set of endmember has been obtained by measurements in situ with a field spectroradiometer
GER 1500. Also, it is proposed the use of new sub-pixel methods based on statistics and certain “units of 
sampling” to apply to the landscapes. The resultant point estimations for these new units will be the 
“observations” and all of them will carry out an special role to simulate the final spectral signature. This 
methodology is used to simulate spectral signatures of a Mediterranean forest landscape near to Madrid 
(Spain). Furthermore the spectral signature model obtained through Field Radiometry data will be 
correlated with the image data of the same zone provided by the Landsat 7 Enhaced Thematic Mapper
Plus (ETM+) sensor once corrected. The results obtained in correlation studies seem to conclude its 
efficiency. At the same time, the results open new research guidelines.
CONCLUSIONS
Mixed hyperplanes for each band have been obtained by linear least squares 
techniques through balanced and unbalanced statistics. 
The modeled spectral signatures are very similar that the one provided by the sensor 
satellite, once atmospheric correction has been made. The simulation forecasts 
signatures values slightly higher than those provided by the satellite sensor in the first 
three bands, whereas in the fourth band this value is slightly lower. The use of 
unbalanced statistic provides values closest to the satellite sensor.
To sum up, it has been proposed a methodology to simulate spectral mixed 
signatures. The methodology derives from the use of new sub-pixel methods based on 
statistics and certain “units of sampling”. Furthermore, correlation studies have been 
carried out with near and remote spectral data in order to evaluate this methodology. 
Their results seem to conclude its efficiency. At the same time, the results open new 
research guidelines.
These simulated mixed signatures will feed a Library Spectral of Land Cover Spectral 
Responses
REFERENCES
Arquero, A., Martínez, E. & Gonzalo, C. 2003. Reducción de datos hiperespectrales de Radiometría de Campo”, Teledetección y 
Desarrollo regional; X Congreso Nacional de Teledetección, Cáceres (Spain): 439-442.
Heinz, D & Chang, C. 2001. Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in 
Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing 39 (3): 529-545.
Keshava, N. 2003.  A Survey of Spectral unmixing Algorithms. Lincoln Laboratory Journal 14(1): 55-78.
Rand, R. S. 2001. A Spectral Mixture Process Conditioned by Gibbs-Based Partitioning. IEEE Transactions on Geoscience and 
Remote Sensing 39 (7): 1421-1433.
Small, C. 2001. Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing
22(7): 1305- 1334.
Vázquez, J. M., Arquero, A., Martínez, E. & Gonzalo, C. 2004. An efficient methodology for endmembers selection by field radiometry: 
an application to multispectral mixture model. Signal and Image Processing for Remote Sensing X 5573: 435-443.
Vázquez, J. M., Arquero, A. & Martínez, E. 2007.  Aplicación de modelos estadísticos a la selección eficiente de cubiertas tipo 
mediante Radiometría de Campo. IEEE Latin America Transactions 5(8): 561-567.
Vermote E.F., Tanré D., Deuzé J.L., Herman M. & Morcrette J.J. 1997. Second Simulation of the Satellite Signal in the Solar Spectrum: 
an overview. IEEE Transactions on Geoscience and Remote Sensing 35(3): 675-686.
IDENTIFICATION OF THE 
LANDSCAPE UNITS TO BE 
ANALYSED
The Global Unit (Gu) has equal 
dimension of the pixel of remote sensor. 
This one will be submitted to a process 
of successive subdivisions, in order to 
get smaller units or Intermediate Units, 
Iu, composed, in turn, by indivisible ones 
called Elemental Units, Eu.
From the sampling information of these 
Eu, it will be possible to model the mixed 
spectral response of Gu. For that, the 
size of each Eu, must be such that 
behaves as a “homogeneous spectral 
unit”. Its spectral content must be as 
close as possible to the endmember in 
order to represent optimally the cover 
itself. High land covers variability will 
involve the existence of a large number 
of very small ones.
DATA
The study zone is situated in the Mountgancedo near Madrid (Spain). This site is located to 
the south-west of Community of Madrid, and it has a surface area of 125 hectares. The 
study area selected is located at 40º 24´30´´ N, and 3º49´50´´ W (4473/4474 N, 429/430 W 
UTM). Several mediterranean species of vegetation can be easily found, such as holm oaks 
(Quercus ilex), spanish lavender (Lavandula pedunculata) and the crimson spot rockrose 
(Cistus ladanifer). The surface is also covered by grass and abundant meadows as well as 
bare soil, and rocks and stones outcrops. The land cover spectral samples have been 
obtained in the summer by Field Radiometry with the GER 1500, agreement with the date of 
Landsat ETM+ remote data register. Sampling in situ has been carried out according to a 
methodology  that optimizes the number of samples to pick-up.
Even though the remote sensor (ETM+) has six bands with the same spatial resolution, only 
the first four ones can be considered according to the spectral register interval of field 
radiometer; in order to correlate field and remote information.
Since the data registered in situ, provides a continuous spectral response, a previous 
reduction methodology based on the integration of the radiance values into the spectral 
bands interval selected, was carried out. Thus, the field spectral data had been reduced to 
field spectral signatures, with four values named R-ETM+1, R-ETM+2, R-ETM+3 y R-
ETM+4 in agreement with the first four bands of the remote sensor.Iu
G
u
Global Unit
MIXED HYPERPLANES
Reflectance values obtained by application of balanced and unbalanced statistics 
to Intermediate Units.
Intermediate Unit
Balanced
Unbalanced
Spectral signature values of simulated models 
(estimated “in situ” percentages of abundances 
18.8% oak, 78.1% meadow, 3.1% shrub) and 
satellite corrected data for the Global Unit
M
E
T
H
O
D
L
O
G
Y
SAMPLING METHODS
The criteria to design the more 
representative statistics for Iu could be 
based on a strategy of "considering" 
more or less the information belonging 
to the different Elementary Units. In this 
sense, we propose the “balanced 
statistic” and the “unbalanced statistic”. 
The first one consists in assigning to 
each Eu, identical "weights”. So, each 
of them equally will contribute to the 
total spectral response of the Iu. The 
second one considers weighing 
“differently” the information contained in 
the centre than the contained in the 
peripheral.
The Global Unit sampling method could 
be as simple as possible. We suggest a 
new method for picking up samples: the 
so-called "double-diagonal“. This model 
reflects with acceptable accuracy the 
variability of the different land covers, 
and thus achieves a good 
representation of the Global Unit.
The regression models for each band 
(mixed hyperplanes) are obtained by 
linear least squares techniques through 
balanced and unbalanced statistics. 
R
E
S
U
L
T
S
regression models
linear least squares techniques
Balanced method Unbalanced method


An efficient methodology to simulate mixed spectral signatures of land covers through Field Radiometry data

Archivo Digital UPM

A Spectral Mixture Process Conditioned by Gibbs-Based Partitioning.

A Survey of Spectral unmixing Algorithms.

An efficient methodology for endmembers selection by field radiometry: an application to multispectral mixture model. Signal and Image Processing for Remote Sensing X

Aplicación de modelos estadísticos a la selección eficiente de cubiertas tipo mediante Radiometría de Campo.

Estimation of urban vegetation abundance by spectral mixture analysis.

Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery.

Reducción de datos hiperespectrales de Radiometría de Campo”, Teledetección y Desarrollo regional; X Congreso Nacional de Teledetección,

Second Simulation of the Satellite Signal in the Solar Spectrum: an overview.

http://oa.upm.es/3446/1/INVE_MEM_2008_54874.pdf

An efficient methodology to simulate mixed spectral signatures of land covers through Field Radiometry data

Abstract

Similar works

Full text

Available Versions

Servicio de Coordinación de Bibliotecas de la Universidad Politécnica de Madrid

Archivo Digital UPM