Gamba, P.

Kneubühler, M.

Müller, A.

Plaza, A.

Schaepman, M.E.

English

Wageningen Yield 

RECENT ACTIVITIES IN THE HYPERSPECTRAL IMAGING NETWORK (HYPER-I-NET):
A EUROPEAN CONSORTIUM FOSTERING IMAGING SPECTROSCOPY RESEARCH
Antonio Plaza1, Paolo Gamba2, Mathias Kneubuehler3, Andreas Mueller4, Michael Schaepman3
1University of Extremadura, Caceres, Spain; 2University of Pavia, Italy
3University of Zurich, Switzerland; 4German Remote Sensing Data Center, Germany
ABSTRACT
The Hyperspectral Imaging Network (HYPER-I-NET) is a
four-year Marie Curie Research Training Network project de-
signed to build an interdisciplinary European research com-
munity focusing on hyperspectral imaging activities. The net-
work is currently formed by a multidisciplinary team com-
posed of fifteen highly experienced European partner organi-
zations. In this paper, we outline the activities that have been
carried out in the four main areas covered by HYPER-I-NET:
1) hyperspectral sensor specification, 2) processing chain def-
inition and implementation, 3) calibration, validation and def-
inition of standardization mechanisms, and 4) science appli-
cations. Along with the description of the progress made in
the four main areas listed above, this paper also describes
some of the training and transfer of knowledge activities car-
ried out during the first two years of the project.
Index Terms— Hyperspectral imaging, education and
training, sensor design, data processing, calibration/validation,
science applications.
1. INTRODUCTION
Imaging spectroscopy [1, 2], also known as hyperspectral
imaging, is concerned with the measurement, analysis, and
interpretation of spectra acquired from a given scene (or
specific object) at a short, medium or long distance by an air-
borne or satellite sensor. The Hyperspectral Imaging Network
(HYPER-I-NET)1 [3] is a four-year Marie Curie Research
Training Network2 designed to build an interdisciplinary
European research community focusing on this technology,
which has opened new perspectives in many Earth Observa-
tion related applications [4]. The network is currently formed
by a multidisciplinary team composed of fifteen highly ex-
perienced European partner organizations, and the theme
of the network is at the confluence of heterogeneous disci-
plines, such as sensor design including optics and electronics,
aerospace engineering, remote sensing, geosciences, com-
puter sciences including high performance computing, signal
1http://www.hyperinet.eu
2http://cordis.europa.eu
processing, and Earth Observation related products. In partic-
ular, activities in this network intend to cover all the different
aspects that comprise the entire hyperspectral data processing
chain, ranging from sensor design and calibration/validation
[5, 6] to advanced data processing [7], and science applica-
tions [8].
Although hyperspectral imaging has been a very active
area recently, we believe that no sufficient attention has been
given to research activities covering the entire data process-
ing chain and, as a result, we feel that the abilities in this area
are fragmented throughout various specialized research teams
and companies, a fact that has largely resulted in the lack of
data standardization and validation procedures. In this regard,
the proposed network aims at providing a timely and unique
opportunity to bridge the gap between the operational proce-
dures of hyperspectral imaging and the development of tech-
niques for efficient data exploitation and management. As a
result, our planned activities are specifically directed towards
overcoming the boundaries between traditionally disjoint dis-
ciplines such as sensor design, data processing and applica-
tion insight. Resulting from this effort, we expect to introduce
new standardized frameworks for hyperspectral data process-
ing and validation.
In this paper, we outline the activities that have been car-
ried out in the four main areas covered by HYPER-I-NET
during the first two years of the project. Along with a detailed
description of the progress made in the four main areas listed
above, this paper also describes the training and transfer of
knowledge activities. Being an educational project, HYPER-
I-NET is particularly focused on the training of young re-
searchers. In this regard, one of the main goals of the project
is to provide young researchers involved in the network with
the required background and expertise on the multiple disci-
plines involved in hyperspectral imaging.
2. SCIENTIFIC AREAS
In this section we provide an overview of the activities in
the four main scientific areas covered by the network. This
general overview serves as an introduction to the main ac-
tivities in the different workpackages that are currently being
addressed by network partners.
Fig. 1. Project website at http://www.hyperinet.eu
1. Hyperspectral sensor specification. The main goal of
this activity is to investigate the sensor requirements
for various applications and develop new sensor spec-
ifications. For this purpose, the tasks carried out com-
prise the analysis of application needs in terms of de-
rived parameters and variables in different application
fields such as agriculture, forestry, geology, phenol-
ogy/limnology or urban management, with the ultimate
goal of compiling spectral databases to determine the
spectral response related to the individual variables and
parameters and to translate the observed needs into per-
formance requirements of new hyperspectral sensors
by developing a dedicated hyperspectral instrument
model (optical layout and transfer, noise, etc.)
2. Processing chain definition and implementation. The
main goal of this activity is to settle the basis for the
generalization of a well-defined hyperspectral data col-
lection and processing chain that might serve as a stan-
dardized procedure for processing this type of data in
Europe. For this purpose, the tasks carried out com-
prise the definition of a processing chain able to ad-
dress needs from scientific applications and constraints
imposed by sensor design, and the preliminary imple-
mentation of the processing chain steps. The expected
outcome of this activity is a series of standardized hard-
ware/software processing techniques able to deal with
the intrinsic complexity of the data, along with process-
ing chain definition and implementation reports.
3. Calibration, validation and definition of standardiza-
tion mechanisms. This activity is focused on the cali-
bration/validation of hyperspectral sensors and the re-
sults from various processing steps of the processing
chain described above. This is a crucial step to re-
duce the overall uncertainties introduced by hyperspec-
tral imaging instruments. For this purpose, the tasks
carried out comprise an inventory of existing calibra-
tion equipment and methodologies as well as an inven-
tory of methods and processors for onboard, laboratory
and vicarious calibration and assimilation, interaction
with scientists and researchers.
4. Science applications. This activity is aimed at com-
piling relevant applications and methods applied using
imaging spectrometer data, and creating a product cat-
alogue of both. The main task carried out in this activ-
ity comprise the definition of an algorithm theoretical
baseline document (ATBD) listing methods used for se-
lected applications, complemented partly by available
models and source code.
It is important to emphasize that the parts of these work-
packages which have been approved for public distribution by
the consortium are available online from the HYPER-I-NET
project website (see Fig. 1). The fact that several of the de-
liverables resulting from the main scientific activities of the
project are publicly available in the form of technical reports3
allows a rapid dissemination of such activities in the wide re-
mote sensing community. The dissemination is supported by
the network newsletter4, which provides a forum for present-
ing the main research and transfer of knowledge activities car-
ried out by the different partners in the consortium.
3. TRAINING AND TRANSFER OF KNOWLEDGE
Being an educational project, one of the main objectives of
HYPER-I-NET during the first two years of the project has
been to provide high-level training in hyperspectral imaging
to young researchers, with the ultimate goal of introducing
novel, standardized approaches and practices for this emerg-
ing research area in Europe. In this regard, the key elements
of the proposed research methodology have been focused on
the development of research and training activities on hyper-
spectral imaging, with the ultimate goal of training young re-
searchers in this area and to allow our appointed young re-
searchers to have significant competitive advantages and the
potential to find better jobs in science and commercial envi-
ronments.
Among other training events, the network has organized
two summer schools in its first two years. The First Hyper-I-
Net School on Hyperspectral Imaging5 was held in Caceres,
Spain, in October 2007, with attendance of more than 100
international researchers. The school included in-depth tuto-
rials and state-of-the-art presentations from leading experts in
the field. The Second Hyper-I-Net School on Earth Science
and Applications Using Imaging Spectroscopy6 was held in
3http://www.hyperinet.eu/publication4.htm
4http://www.hyperinet.eu/publication5.htm
5http://www.hyperinet.eu/event1.htm
6http://www.hyperinet.eu/event3.htm
Fig. 2. Summerschool in Wageningen, The Netherlands.
Fig. 3. Multimedia course in the e-learning platform.
Wageningen, The Netherlands, in September 2008, with at-
tendance of more than 80 international researchers (see Fig.
2). During the two summer schools, young researchers had
the opportunity to interact with more experienced ones and
receive training on specialized instruments and equipment.
These activities served as a basis for providing young re-
searchers with specialized training through their operation of
equipment facilitated by networks partners, including hyper-
spectral cameras, field spectrometers and other instruments
that could be easily shared by young researchers. In addition
to this, hands-on practice sessions were organized, including
tutorials on hyperspectral data processing and calibration,
data collection, ground campaigns, etc.
4. DISTRIBUTED E-LEARNING PLATFORM
A distributed e-learning platform (see Fig. 3) offering a
comprehensive learning environment to young researchers
and external partners has been developed by HYPER-I-NET
partners, supported by Opera Multimedia7. This platform
is used to provide multimedia courses and resources on dif-
ferent topic areas related with the project. The platform is
available via login/password from the main project website.
Most partners offer a multimedia course (audio and video)
broken down into several units. Each unit is treated as a sep-
arate learning object which can be accessed independently so
that users can decide which unit to access and in which order
7http://www.operamultimedia.com
(flexible training path). Each course is accompanied by a
pre-assessment and post-assessment in which the fellows can
self-assess their progress. A total of 13 multimedia courses
(summarized below) have been designed by HYPER-I-NET
partners and are currently available in the e-learning platform:
• Urban Remote Sensing. This course provides a first in-
troduction of urban remote sensing and data fusion re-
lated to urban area analysis using remotely sensed data.
A more specific analysis is then dedicated to hyperspec-
tral data and their processing challenges.
• Remote Sensing Measurements of Vegetation. This
course addresses the relevant topic of how to char-
acterize vegetation parameters using remotely sensed
hyperspectral image data.
• Retrievals of the ecosystem variables from the remotely
sensed (hyper)spectral data. This e-learning course
provides insight into the quantitative remote sensing of
vegetation of terrestrial ecosystems.
• Quantitative remote sensing. This course introduces
the use of (hyperspectral) remote sensing for deriving
quantitative information on biophysical and biochem-
ical properties of vegetation. The course highlights
current developments and challenges in quantitative
remote sensing of vegetation.
• GIS Module. This module provides the theoretical
background that remote sensing specialists will require
when working with geographic information systems.
• Calibration and characterization of hyperspectral cam-
eras. This course provides an overview of methods for
calibrating and characterizing hyperspectral cameras of
the “pushbroom” type. In includes theory as well as
practical examples.
• High performance computing for hyperspectral imag-
ing. This course focuses on the development and ef-
ficient implementation of algorithms for hyperspectral
imaging, with emphasis on endmember extraction and
spectral unmixing.
• Hyperspectral Imaging Techniques. This course starts
with a short hyperspectral history and then gives an
introduction into the physical basics, necessary to un-
derstand hyperspectral sensor principles. It then pro-
vides insight into instrument development and design
aspects. The course closes with an overview of existing
and future sensors.
• Campaign Planning and Operations. In this course,
the fundamental processes for the preparation and the
realization of an airborne hyperspectral survey are pre-
sented. This includes planning of a whole airborne hy-
perspectral survey as well as single data acquisitions,
planning of a field campaign and introduction on field
methods for remote sensing, general campaign man-
agement, calibration and operation of a sensor.
• Hyperspectral Unmixing. This course addresses the hy-
perspectral unmixing inverse problem, which, given a
set of hyperspectral vectors, is the inference of the num-
ber of reference substances, also called endmembers,
their spectral signatures, and their abundance fractions.
• Hyperspectral camera technology. This course gives
a basic introduction to the technology of hyperspectral
imagers. Emphasis is given to the topics of imaging
optics and photodetectors. Examples are used to illus-
trate the effect of camera parameters on the outcome of
hyperspectral image processing.
• Applications of hyperspectral imaging to geologic and
coastal environments. This course presents some ex-
amples on how imaging spectrometer data can be used
to map information relevant to geologic and coastal en-
vironments.
• Source separation with Independent Component Anal-
ysis. This course focuses on the adaptation of source
separation techniques to the analysis of hyperspectral
data, with particular emphasis on the characterization
of mixed pixels and the estimation of fractional abun-
dances.
5. SUMMARY AND FUTURE EVENTS
We have described recent activities in HYPER-I-NET, a re-
cently funded Marie Curie Research Training Network which
focuses both on educational aspects and on the scientific goals
which are achieved through training in all the research areas
that comprise the entire hyperspectral processing chain, from
sensor design and flight operation to data collection, process-
ing, management and interpretation. Our focus has been on
providing an overview of the scientific aspects that are being
covered by the project, as well as on describing the main train-
ing and transfer of knowledge events carried out during the
first two years of the project. In addition, we have described
the project e-learning platform comprising a set of multimedia
courses used to provide multimedia courses and resources on
different topic areas related with the project. These activities,
along with the research work carried out by the 12 early-stage
researchers and 5 experienced researchers appointed in the
project, is expected to introduce new methods for hyperspec-
tral data collection, processing, validation and exploitation,
thus creating an impact on the design of future and on-going
remote sensing and exploration missions.
In the future, the network will continue pursuing the re-
search and training/transfer of knowledge activities described
in the scientific workplan. The increasing impact and rel-
evance of the research work carried out by HYPER-I-NET
fellows8 allows us to anticipate that the young researchers
trained within the network will have the potential to lead the
future expansion in the use of this technology.
6. ACKNOWLEDGEMENT
This work has been supported by the European Community’s
Marie Curie Research Training Networks Programme under
reference MRTN-CT-2006-035927, Hyperspectral Imaging
Network (HYPER-I-NET).
7. REFERENCES
[1] A. F. H. Goetz, G. Vane, J. E. Solomon, B. N. Rock, “Imaging
spectrometry for Earth remote sensing,” Science, vol. 228, pp.
1147-1153, 1985.
[2] M. Schaepman, S. L. Ustin, A. Plaza, T. Painter, J. Verrelst and
S. Liang, “Earth system science related imaging spectroscopy-
An assessment,” Remote Sensing of Environment, in press,
2009.
[3] A. Plaza, A. Mueller, T. Skauli, Z. Malenovsky, J. Bioucas, S.
Hofer, J. Chanussot, V. Carrere, I. Baarstad, J. Nieke, T. Hyvari-
nen, P. Gamba, J. A. Benediktsson, M. E. Schaepman, B. Zaga-
jewski, “HYPER-I-NET: European research network on hyper-
spectral imaging,” Proceedings of the IEEE International Geo-
science and Remote Sensing Symposium (IGARSS), pp. 4790-
4793, Barcelona, Spain, July 2007.
[4] R. O. Green et al., “Imaging spectroscopy and the airborne vis-
ible/infrared imaging spectrometer (AVIRIS),” Remote Sensing
of Environment, vol. 65, pp. 227-248, 1998.
[5] R. Richter, A. Mueller, M. Habermeyer, S. Dech, K. Segl,
H. Kaufmann, “Spectral and radiometric requirements for the
airborne thermal imaging spectrometer ARES,” International
Journal of Remote Sensing, vol. 26, pp. 3149, 2005.
[6] K. I. Itten, F. DellEndice, A. Hueni, M. Kneubuehler, D.
Schlpfer, D. Odermatt, F. Seidel, S. Huber, J. Schopfer, T.
Kellenberger, Y. Bhler, P. D’Odorico, J. Nieke, E. Alberti, K.
Meuleman, “APEX - the Hyperspectral ESA Airborne Prism
Experiment,” Sensors, vol. 8, pp. 6235-6259, 2008.
[7] P. Gamba, A. Plaza, J. A. Benediktsson, J. Chanussot, “Euro-
pean perspectives in hyperspectral data analysis,” Proceedings
of the IEEE International Geoscience and Remote Sensing Sym-
posium (IGARSS), pp. 4794-4797, Barcelona, Spain, July 2007.
[8] L. J. Kooistra, G. W. W. Wamelink, G. Schaepman-Strub, M. E.
Schaepman, H. F. Dobben, U. Aduaka, O. Batelaan, “Assessing
and predicting biodiversity in a floodplain ecosystem: Assimi-
lation of net primary prodution derived from imaging spectrom-
eter data into a dynamic vegetation model,” Remote Sensing of
Environment, vol. 112, pp. 2118-2130, 2008.
8http://www.hyperinet.eu/publication3.htm


Assessing and predicting biodiversity in a floodplain ecosystem: Assimilation of net primary prodution derived from imaging spectrometer data into a dynamic vegetation model,”

Earth system science related imaging spectroscopyAn assessment,” Remote Sensing of Environment, in press,

European perspectives in hyperspectral data analysis,”

Imaging spectrometry for Earth remote sensing,”

Imaging spectroscopy and the airborne visible/infrared imaging spectrometer

Spectral and radiometric requirements for the airborne thermal imaging spectrometer

Recent activities in the hyperspectral imaging network (Hyper-I-Net): A European consortium fostering imaging spectroscopy research

Crossref

Recent activities in the Hyperspectral Imaging Network (HYPER-I-NET): A European consortium fostering imaging spectroscopy research

NARCIS 

A. Plaza

M. Kneubühler

A. Mueller

M. Schaepman

GAMBA, PAOLO ETTORE

Archivio Istituzionale della Ricerca - Università degli Studi di Pavia

Recent Activities in the Hyperspectral Imaging Network (HYPER-I-NET): A European Consortium Fostering Imaging Spectroscopy Research

Name not available

Recent activities in the hyperspectral imaging network (Hyper-I-Net): A European consortium fostering imaging spectroscopy research

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Wageningen Yield

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NARCIS

Archivio Istituzionale della Ricerca - Università degli Studi di Pavia

Name not available