Espectro-Radiometría y Espectro-Radiometría y Teledetección

Espectro-Radiometría y Espectro-Radiometría y

Centro de Ciencias Humanas y SocialesConsejo Superior de Investigaciones CientíficasMadrid, 3-4 de Diciembre de 2009

Antonio PlazaAntonio PlazaUniversidad de ExtremaduraUniversidad de Extremadura

EE--mail: [email protected] mail: [email protected] http://www.umbc.edu/rssipl/people/aplazahttp://www.umbc.edu/rssipl/people/aplaza

Espectro-Radiometría y Teledetección Hiperespectral

Espectro-Radiometría y Teledetección Hiperespectral

ContentsContents1. Introduction to hyperspectral imaging

2. Specific challenges of hyperspectral data processing

3. Spectral unmixing techniques for hyperspectral data analysis

1. Introduction to hyperspectral imaging



Hyperspectral image analysis: contents and distribution

4. Algorithm demonstrations

5. Summary and remarks

6. The Hyperspectral Imaging Network (Hyper-I-Net)

7. Related special issues in specialized journals





Seminario: Avances en Espectro-Radiometría, CSIC, Madrid, 3-4 Diciembre de 2009

What is hyperspectral remote sensing?What is hyperspectral remote sensing?“Imaging Spectroscopy (hyperspectral remote sensing) deals with data from instruments acquiring reflectance images in a large (>40) number of narrow (<0.01 to 0.02 µm. in width), contiguous (i.e., adjacent) spectral bands allowing to derive the mineralogy of objects or obtaining information on soil, water and biochemical composition.”

“Imaging Spectroscopy (hyperspectral remote sensing) deals with data from instruments acquiring reflectance images in a large (>40) number of narrow (<0.01 to 0.02 µm. in width), contiguous (i.e., adjacent) spectral bands allowing to derive the mineralogy of objects or obtaining information on soil, water and biochemical composition.”

Introduction to hyperspectral imaging: increased spectral resolution

“The challenge of mapping minerals on any moon or planet with hyperspectral imaging relies onsub-pixel characterizationof spectral features fromthousands of possibilities.”

Lesson (not learned): The advantage of the concept (of imagingspectrometry) is that no morecommittees need to be formed to develop a rationale for particular spectral bands and to compromiseamongdisciplines whenit comes time to build the sensor’ [Goetz, 1992]

“The challenge of mapping minerals on any moon or planet with hyperspectral imaging relies onsub-pixel characterizationof spectral features fromthousands of possibilities.”

Lesson (not learned): The advantage of the concept (of imagingspectrometry) is that no morecommittees need to be formed to develop a rationale for particular spectral bands and to compromiseamongdisciplines whenit comes time to build the sensor’ [Goetz, 1992]

soil, water and biochemical composition.” Alexander F. H. Goetz, IGARSS 2006soil, water and biochemical composition.” Alexander F. H. Goetz, IGARSS 2006

Seminario: Avances en Espectro-Radiometría, CSIC, Madrid, 3-4 Diciembre de 2009 1

Spectral mixture analysis: Determines the abundance of materials (e.g. precision agriculture).

Characterization: Determines variability of identified material (e.g. wet/dry sand, soil particle size effects).

Identification: Determines the unique identity of

Spectral mixture analysis: Determines the abundance of materials (e.g. precision agriculture).

Characterization: Determines variability of identified material (e.g. wet/dry sand, soil particle size effects).

Identification: Determines the unique identity of HyperspectralHyperspectral

Levels of Spectral Information in Remote Sensing

Ultraspectral

(1000’s of bands)

Ultraspectral

(1000’s of bands)


Identification: Determines the unique identity of the foregoing generic categories (e.g. land-cover or mineral mapping).

Discrimination: Determines generic categories of the foregoing classes.

Classification: Separates materials into spectrally similar groups (e.g., urban data classification).

Detection: Determines the presence of materials, objects, activities, or events.

Identification: Determines the unique identity of the foregoing generic categories (e.g. land-cover or mineral mapping).

Discrimination: Determines generic categories of the foregoing classes.

Classification: Separates materials into spectrally similar groups (e.g., urban data classification).

Detection: Determines the presence of materials, objects, activities, or events.

PanchromaticPanchromatic

Hyperspectral

(100’s of bands)

Hyperspectral

(100’s of bands)

MultispectralMultispectral

(10’s of bands)

MultispectralMultispectral

(10’s of bands)


Algorithms +Efficient implementations



AVIRIS (NASA/JPL) Hyperspectral Cubehttp://aviris.jpl.nasa.gov/html/aviris.freedata.html



Hyperspectral data used for demonstration:Introduction to hyperspectral imaging: increased spectral resolution

Data set provided by Robert O. Green at NASA/JPL Reference information available from U.S. Geological Survey

AVIRIS data over lower Manhattan (09/15/01) Spatial location of thermal hot spots in WTC area


Fire Temperatures

Asbestos

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Wavelength (nm)

AVIRISEstimateResidual

WTC Hot Spot Area AHottest Spectrum

Temperature Estimate=928K6% of the area

September 11th World Trade Center

Challenges of hyperspectral data processing: computing

Debris CompositionAsbestos

AVIRIS spectra were used to measure fire temperature, asbestos contamination, and debris spread.


















Presence of mixed pixels in hyperspectral data

Pure pixel(water)

Mixed pixel(soil + rocks)

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Challenges of hyperspectral data processing: mixed pixels

Mixed pixel(vegetation + soil)

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Some particularities of hyperspectral data are not to be found in other types of image data:• Mixed pixels (due to insufficient spatial resolution and mixing effects in surfaces)• Sub-pixel targets (very important and crucial in many hyperspectral applications)


Integragion of spatial and spectral information• Much effort has been given to processing hyperspectral image data in spectral terms.

• Data analysis is carried out without incorporating information about spatial context.

Pixel spatial coor-dinates randomly

shuffled

Challenges of hyperspectral data processing: integration

• There is a need to incorporate the image representation of the data in the analysis.

• Most available approaches consider spatial and spectral information separately.

• Several approaches presented in this course to achieve the desired integration.

Spectral processing Spectral processingSame output results


Why high performance computing is crucial?Biomass Burning: Sub-pixel temperatures and extent, smoke, combustion products�

Environmental Hazards: Contaminants (direct and indirect), geological substrate�

Coastal and Inland Waters: Chemical and biological standoff detection, oil spill monitoring and tracking...

Ecology: Chlorophyll, leaf water, lignin,

Biomass Burning: Sub-pixel temperatures and extent, smoke, combustion products�

Environmental Hazards: Contaminants (direct and indirect), geological substrate�

Coastal and Inland Waters: Chemical and biological standoff detection, oil spill monitoring and tracking...

Ecology: Chlorophyll, leaf water, lignin,


Wildland Fires in Spain/Portugal (August 2005) Imaged by MERIS sensor, European Space Agency

Ecology: Chlorophyll, leaf water, lignin, cellulose, pigments, structure, nonphotosynthetic constituents�

Commercial Applications: Mineral exploration, agriculture and forest status�

Military Applications: Detection of land mines, tracking of targets, decoys...

Others: Human infrastructure, Medical...

Ecology: Chlorophyll, leaf water, lignin, cellulose, pigments, structure, nonphotosynthetic constituents�

Commercial Applications: Mineral exploration, agriculture and forest status�

Military Applications: Detection of land mines, tracking of targets, decoys...

Others: Human infrastructure, Medical...


• Parallel computer: a collection of processing elements that cooperate to solve problems faster

• Hyperspectral imaging demands parallel computers to speed-up many applications

• Speed-up (p processors) = Performance (p processors)Performance (1 processor)

Parallel computing using commodity clustersChallenges of hyperspectral data processing: computing

Earth Simulator (5120 processors)NASA Portable MiniCluster (16 processors)


The future: onboard processing with specialized hardware





















Soil

Tree

Grass

Macroscopic mixture:

12 meters12

me t

ers

4 meters

4 m

eter

s

Intimate mixture:

Presence of mixed pixels in hyperspectral dataSpectral unmixing techniques: presence of mixed pixels

Macroscopic mixture:

15% soil, 25% tree, 60% grass in a 3x3 meter-pixel

Intimate mixture:

Minerals intimately mixed in a 1-meter pixel

Increasing the spatial resolution of the sensor does not necessarily solve the mixture problem

• Mixed pixls can still be obtained at very high spatial resolutions (may complicate analysis)• Intimate mixtures may take place regardless of the spatial resolution available• Models for mixed pixel characterization are required. Two strategies adopted:

ü Linear mixture model (generally unsupervised)ü Nonlinear mixture model (generally supervised, needs prior information)


Linear versus nonlinear mixing.-• Linear mixture model

ü Assumes that endmember substances are sitting side-by-side within the FOV.• Nonlinear mixture model

ü Assumes that endmember components are randomly distributed throughout the FOV.ü Multiple scattering effects.

Spectral unmixing techniques: linear versus nonlinear unmixing

Linear interaction

( ) )y,x(yx,)y,x( nMf +α= ( ) )y,x(yx,)y,x( nMf +α=

Nonlinear interaction

( )[ ] )y,x(yx, ,F)y,x( nMf +α= ( )[ ] )y,x(yx, ,F)y,x( nMf +α=

Linear mixture Nonlinear mixture


Linear spectral unmixing (LSU).-

• The goal is to find extreme pixel vectors (endmembers) that can be used to “unmix” other mixed pixels in the data using a linear mixture model.

• Each “mixed” pixel can be obtained as a combination of endmember fractional abundances. A crucial issue is how to find spectral endmembers.

1e

Spectral unmixing techniques: linear spectral unmixing


Band a

Ban

d b

2e

3e


Linear interaction


Linear mixture

Standard linear mixture-based analysis (unsupervised).-

Spectral unmixing techniques: linear unmixing methodology


Extreme pixel

Extreme pixel

Skewer 1

Skewer 2

Pixel Purity Index (PPI) algorithm.-Spectral unmixing techniques: linear unmixing methodology

Extreme pixel

Extreme pixel

Skewer 3

1) Number of ske-wers to be generated by the algorithm (k)

2) Cut-off endmem-ber threshold (t)

parameters


Spectral unmixing techniques: linear unmixing methodology

Demo: endmember extraction & unmixingDemo: endmember extraction & unmixing


Demo will be performed using ITTVIS Envi 4.5 (http://www.ittvis.com)

Nonlinear spectral unmixing.-

• The goal is to learn the properties of the mixtures in the original hyperspectral data, in order to understand nonlinearities in the process.

• We have approached this problem using supervised learning, in which highly descriptive (mixed) samples are used as input training patterns.

Spectral unmixing techniques: nonlinear spectral unmixing


Extreme


Nonlinear interaction


Nonlinear mixture Core

Border

Band 1

Ban

d 2


Dimensionality reduction

PCA, MNF, DWT, ICA,…

Artificial neural networks for nonlinear unmixing.-• Incorporates a learning process based on known training samples:


TrainingSamples

TestSamples

Artificial Neural Network Classifier

Test classification accuracy

Randomly selected


Architecture #1 (combined linear/nonlinear unmixing).-• Nonlinear refinement of linear abundance estimations (using FCLSU).• Limitation: this approach does not take into account the full spectral information.

p-dimensionalabundance

Estimation of number of endmembers, p

Automatic endmember


Intelligent training sample selection algorithm

Identification of the mosteffective training samples

Artificial neural network (p input and p output neurons)

Training

abundance data set

Initialcondition

Nonlinear refinement

n-dimensionalspectral data set

Automatic endmember extraction algorithm

Fully constrained linear spectral unmixing

p endmembers


Architecture #2 (nonlinear spectral unmixing).-• MLP initially trained using only p endmembers provided by an automatic algorithm.

• Nonlinear refinement from a linear initial weight condition using training samples.

• The full spectral information is used throughout the process.

ü Choice of endmember finding algorithms for initialization

ü Choice of intelligent training sample selection algorithms

Automatic endmember


n-dimensionalspectral data set

Automatic endmember extraction algorithm

p fractional abundance

planes

p endmembers

Artificial neural network (n input and p output neurons)

Initial condition

Intelligent training sample selection algorithm

Identification of the most effective training samples

Nonlinear refinement

Mustard�s database with known proportions.-• Database of 26 pure and mixed (binary & ternary) spectra.

ü Collected using RELAB, a bidirectional spectrometer.

ü 211 spectral bands in the range 0.4 – 2.5 µm.ü Ground-truth information about fractional abundances is available for each spectra.

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Olivine Enstantite Magnetite Anorthosite


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ce


True abundance proportions and selection order.-

Pure signatures

Binary mixtures


Ternary mixtures


Abundance estimation using linear mixture model.-• Fully constrained linear spectral unmixing(FCLSU):

ü Generally accurate predictions for binary mixtures.

ü Ternary mixtures were not accurately modeled.

ü Overall RMSE scores:

Ø 11.3% (Anorthosite); 9.1% (Enstatite); 6.2% (Olivine)

Ø Unconstrained linear models always produced higher errors


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Initial estimation (Anorthosite)

Tru

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(Ano

rtho

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)

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livin

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Enstatite/OlivineAnorthosite/OlivineAnorthosite/Enstatite/Olivine

Ø Unconstrained linear models always produced higher errors


Results using architectures #1 & #2.-• Architecture #1 : Starting from linear model, samples were progressively incorporated.

ü Similar results to fully constrained linear mixture model (FCLSU)ü No significant improvements in binary and ternary mixturesü Dependent on the training samples used for modeling such mixtures

• Architecture #2 : Does not use the linear mixture model at all.ü Much better results


ü Depends on the process of selecting training samples:

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RM

SE

MSAOSPMaximin

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DAIS/ROSIS data collected at different altitudes.-• Obtained in of 2001 within HySens campaign of DLR at Extremadura, Spain.• Dehesa semi-arid ecosystem formed by cork-oak trees, soil and pasture.

University of Extremadura


Guadiloba reservoir

Dehesa area


DAIS/ROSIS data collected at different altitudes.-DAIS low resolution (6 meters) DAIS high resolution (3 meters)


ROSIS low resolution (2.4 meters) ROSIS high resolution (1.2 meters)


Reference data generation.-1. ROSIS high resolution image roughly classified into soil, cork-oak , pasture (maximum likelihood).

2. ROSIS high resolution image (1.2 m) was registered to DAIS low resolution (6 m) using an automated ground control point-based method with sub-pixel accuracy.

3. ML-generated map for ROSIS was used to provide an initial estimation of land-cover classes in the DAIS image (6x6-meter grid overlaid on 1.2x1.2-meter grid).

4. Abundance maps at the ROSIS level were thoroughly refined using field data:

a) Field spectra collected for several areas using ASD FieldSpec Pro spectro-radiometer


b) Ground estimations of pasture abundance in selected sites of known dimensions

c) High-precision GPS work , spectral sample collection, harvest procedures, expert knowledge


Abundance estimation using linear mixture model.-• Spectral range from 504 to 864 nmwas selected for experiments (adequate for

landscape and very well covered by the two considered sensors through narrow spectral bands).

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504 544 584 624 664 704 744 784 824 864

Reflectance

Cork-oak treePastureSoil


• Fully constrained linear spectral unmixing(FCLSU) better than unconstrained linear models.Wavelength (nm)

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Cork-oak tree

RMSE=16.9%0,0

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Pasture Soil

RMSE=9.3% RMSE=9.5%


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Cork-oak tree

Cork-oak tree

Pasture

Pasture

Soil

Soil

RMSE=10.3% RMSE=9.3% RMSE=9.5%

Borde:

Mezcla:


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sCork-oak tree Pasture Soil



Mezcla:

Espacial:


Summary of results using nonlinear unmixing.-• How many pixel-level predictions fall within a given error bound (in percentage)

of the actual field measurements?

40%

50%

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70%

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100%P

erce

ntag

e


• Highest percentage of pixels with prediction errors below 2% achieved with mixed samples.• With the increase of error bound, spatial samples outperformed mixed samples. • Incorporation of spatial information in training sample selection can minimize the

global error, although local prediction performance may decrease in highly nonlinear classes.

10%

20%

30%

40%

2% 6% 10% 14% 18% 22% 26% 30%

Error bound

BTA MSA MEA FCLSU


Endmember extraction and unmixing practical.-Skewer 1Skewer 1

Skewer 2Skewer 2

Skewer 3Skewer 3

Extreme pixel

Extreme pixel

Extreme pixel

Extreme pixel

Extreme pixelExtreme pixel

Extreme pixelExtreme pixel

Extreme pixel

Extreme pixel

Extreme pixel

Extreme pixel

Extreme pixel

Extreme pixel

Extreme pixel

Extreme pixel

Spectral unmixing techniques: additional information & source codes

Pixel Purity Index (PPI) N-FINDR algorithm

http://www.hyperinet.eu/event1.htmlLook for “Files practice” including:

1) Matlab code for all algorithms2) Simulated hyperspectral data3) Real hyperspectral data4) Step-by-step practice session5) Exercises and questions


















Summary and RemarksSummary and Remarks• The special characteristics of hyperspectral images pose new processing problems, not

found in other types of remote sensing data.

• Endmember extraction and spectral unmixing allows using the increased spectral information to overcome the limitations imposed by spatial resolution (this characteristic is not found in many other types of remote sensing data)

• The integration of spatial and spectral information allows for the development of enhanced supervised/unsupervised analysis techniques.

• The special characteristics of hyperspectral images pose new processing problems, not found in other types of remote sensing data.

• Endmember extraction and spectral unmixing allows using the increased spectral information to overcome the limitations imposed by spatial resolution (this characteristic is not found in many other types of remote sensing data)

• The integration of spatial and spectral information allows for the development of enhanced supervised/unsupervised analysis techniques.

Summary and remarks

of enhanced supervised/unsupervised analysis techniques.

• Further developments are also available and expected in nonlinear spectral unmixing.

• Advances in high performance computing environments including clusters of computers and distributed grids, as well as specialized hardware modules such as field programmable gate arrays (FPGAs) or graphics processing units (GPUs) will also be crucial in many applications.

• Techniques presented in this seminar show the increasing sophistication of a field that is rapidly maturing at the intersection of many different disciplines.

of enhanced supervised/unsupervised analysis techniques.

• Further developments are also available and expected in nonlinear spectral unmixing.

• Advances in high performance computing environments including clusters of computers and distributed grids, as well as specialized hardware modules such as field programmable gate arrays (FPGAs) or graphics processing units (GPUs) will also be crucial in many applications.

• Techniques presented in this seminar show the increasing sophistication of a field that is rapidly maturing at the intersection of many different disciplines.


HYPER-I-NET: European Research

A. Plaza, A. Mueller, R. Richter, T. Skauli, Z. Malenovsky, J. Bioucas, S. Hofer, J. Chanussot, C. Jutten, V. Carrere, I. Baarstad, P. Kaspersen, J. Nieke, K. Itten, T. Hyvarinen, P. Gamba, F. Dell’Acqua, J. A. Benediktsson, M. E. Schaepman,

J. Clevers and B. Zagajewski

HYPER-I-NET: European Research Network on Hyperspectral Imaging

Consortium

Kayser-Threde Gmbh (KT), GERMANY

Instituto Superior Técnico (IST), PORTUGAL

Institute of Systems Biology and Ecology (ISBE), CZECH REPUBLIC

Norwegian Defence Research Establishment (FFI), NORWAY

German Remote Sensing Data Center (DLR), GERMANY

University of Extremadura (UEX), SPAIN

Participating institutions:

Stefan Hofer

José Bioucas Dias

Zbynek Malenovsky

Torbjorn Skauli

Andreas Mueller

Antonio Plaza

Scientist in charge:

Bogdan ZagajewskiWarsaw University (WURSEL), POLAND

Wageningen University (WUR), THE NETHERLANDS

Norsk Elektro-Optics (NEO), NORWAY

University of Iceland (UNIS), ICELAND

University of Pavia (UNIPV), ITALY

Spectral Imaging Oy, Ltd. (SPECIM), FINLAND

Remote Sensing Laboratories, University of Zurich (UZH), SWITZERLAND

Laboratory of Planetology and Geodynamics (CNRS), FRANCE

Technical Institute of Grenoble (INPG), FRANCE

Jan Clevers

Peter Kaspersen

Jon Atli Benediktsson

Paolo Gamba

Timo Hyvarinen

Michael Schaepman

Veronique Carrere

Jocelyn Chanussot

Seminario: Avances en Espectro-Radiometría, CSIC, Madrid, 2009

Main goals1. Bridge the gap between sensor design, hyperspectral data processing, and

science applications in remote sensing activities in Europe

2. Develop standardized and innovative techniques/products for hyperspectral image analysis

3. Establish standardized data processing and validation/quality 3. Establish standardized data processing and validation/quality mechanisms in all the steps of the hyperspectral processing chain

4. Integrate knowledge from different disciplines (e.g., sensor design, data processing, scientific applications)

5. Improve the cooperation and transfer of knowledge (ToK) from research centres and university groups to SMEs

6. Create a powerful multidisciplinary synergy


The main objectives of our project, as described in Annex I to the contract or “Description of Work” are as follows. First, … Summarizing, the project aims at introducing a multidisciplinary collaboration in the field involving experts from many different disciplines.

Scientific areas

HYPERSPECTRAL SENSOR SPECIFICATION

Investigate the sensor requirements for various applications and develop

new sensor specifications

Coordinator: DLR

HYPERSPECTRAL PROCESSING CHAIN

Develop well-defined hyperspectral data processing chains to be used as

standardized procedures

Coordinator: University of Pavia

COORDINATION

University of

Extremadura

CALIBRATION AND VALIDATION

Calibration/validation of hyperspectral sensors and the result from steps of the processing chains

Coordinator: University of Zurich

SCIENCE APPLICATIONS

Explore relevant applications using imaging spectrometer data, and create

an application catalog

Coordinator: Wageningen University


Network-wide training• Technical seminars for the collaboration of PhD students (arranged

to coincide with management board progress meetings)

• Secondment programme (in accordance with personal career development plans)

• Summer schools (four international joint schools)

• Summer camps (four summer camps will be held within summer schools and connected to ongoing imaging campaigns)

• Round-robin calibration experiments (during summer camps)

• Occasional short visits of studentes to other network partners

• Mid-term and final network workshops

• Training courses


In order to achieve successful training of ESRs, which is one of the main objectives of our project, we have made provision for several activities. As you have noticed in the previous ER description, all ER contracts are partly allocated to WP7 on implementation of local and network-wide training measures, and are therefore expected to be closely involved in the training of ESRs.

Website & e-learning

http://www.hyperinet.eu


Website & e-learning

http://www.hyperinet.eu


















Announcement: IEEE TGRS Special Issue

Call for papers IEEE Transactions on Geoscience and Remote Sensing

Special Issue on Spectral Unmixing of Remotely Sensed Data

Submission deadline: 30 September 2010 • Linear and nonlinear mixture models for analysis of remotely sensed data • Incorporation of spectral similarity measures in spectral mixture modeling • Data dimensionality issues for spectral mixture analysis • Automatic and semi-automatic endmember extraction in remotely sensed data • Supervised endmember extraction and pure class modeling • Adaptive endmember selection and multiple endmember spectral mixture analysis • Unconstrained versus constrained fractional abundance estimation in remotely sensed data • Blind source separation and its relation with spectral unmixing of remotely sensed data

Incorporation of sparsity and spatial information in spectral unmixing of remotely sensed data

Antonio J. Plaza University of Extremadura E-10071 Cáceres, SPAIN

Phone: (+34) 927 257000 (51662) Fax: (+34) 927 257202 E-mail: [email protected]

http://www.umbc.edu/rssipl/people/aplaza

Qian Du Mississippi State University

MS 39762, USA Phone: (+1) 662 325 2035 Fax: (+1) 662 325 2298

Email: [email protected] http://www.ece.msstate.edu/~du

Jose M. Bioucas Dias Technical University of Lisbon 1049-001 Lisbon, PORTUGAL Phone: (+351) 218418466 Fax: (+351) 218418472 E-mail: [email protected]

http://www.lx.it.pt/~bioucas

Xiuping Jia Australian Defence Force Academy Canberra, ACT 2600, AUSTRALIA

Phone: (+61) 2 626 88202 Fax: (+61) 2 626 88443 E-mail: [email protected]

http://www.itee.adfa.edu.au/staff/jiax/

Fred A. Kruse Arthur Brant Laboratory

University of Nevada, Reno, USA Phone: (+1) 303-499-9471 Fax: (+1) 970-668-3614 Email: [email protected]

http://www.mines.unr.edu/able/people/

• Incorporation of sparsity and spatial information in spectral unmixing of remotely sensed data • Quantitative assessment of spectral unmixing • Statistical validation of spectral mixture analysis models • Extension of spectral unmixing to multispectral scenes • Applications of spectral mixture analysis of remotely sensed data • Analysis of intimate mixtures in remotely sensed data: soil, vegetation and other application-specific studies • Spectral unmixing in planetary exploration • High performance computing implementations of spectral unmixing techniques Inquiries about the Special Issue may be directed to the Guest Editors listed below. Papers can be submitted using the manuscript central web link: http://mc.manuscriptcentral.com/tgrs and selecting Spectral Unmixing Special Issue from the ‘Manuscript type’ pull-down menu.


IEEE J-STARS Special Issue Call for Papers:IEEE J-STARS Special Issue Call for Papers:

April 30, 2010

Announcement: IEEE J-STARS Special Issue


Acknowledgement

AgradecimientosAgradecimientos• Proyecto European Research Netowork on Hyperspectral Imaging (MRTN-CT-2006-

035927) para formación de investigadores en análisis de imágenes hiperespectrales • Proyecto European Research Netowork on Hyperspectral Imaging (MRTN-CT-2006-

035927) para formación de investigadores en análisis de imágenes hiperespectrales

• Proyecto EODIX (AYA2008-05965-C04-02) financiado por el Ministerio de • Proyecto EODIX (AYA2008-05965-C04-02) financiado por el Ministerio de


• Proyecto EODIX (AYA2008-05965-C04-02) financiado por el Ministerio de Educación y Ciencia y en el que participan grupos de las Universidades de Valencia, Jaume I de Castellón y Extremadura (proyecto liderado por el Prof. José Sobrino)

• Proyecto EODIX (AYA2008-05965-C04-02) financiado por el Ministerio de Educación y Ciencia y en el que participan grupos de las Universidades de Valencia, Jaume I de Castellón y Extremadura (proyecto liderado por el Prof. José Sobrino)

• Proyecto PRIPRI09A110 financiado por la Junta de Extremadura• Proyecto PRIPRI09A110 financiado por la Junta de Extremadura

• Andreas Mueller (DLR), John Mustard (Brown University) y Robert O. Green (JPL) por proporcionar los datos hiperespectrales utilizados durante el seminario

• Andreas Mueller (DLR), John Mustard (Brown University) y Robert O. Green (JPL) por proporcionar los datos hiperespectrales utilizados durante el seminario

Documents

Espectro-Radiometría y Espectro-Radiometría y Teledetección