Analysis of interferometric hyperspectral images for atmosphere monitoring - GIPSA-lab
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PhD Thesis
Analysis of interferometric hyperspectral images
for atmosphere monitoring
Keywords Signal and Image Processing; Inverse Problems; Hyperspectral Imaging; Interferometric Spectroscopy
(Fourier Transform spectroscopy); Atmosphere parameters retrieval; Remote Sensing
Context and Application Remote sensing from space has been growing ever since the first weather satellites
in the 1970’s, giving access to ever more detailed information on the Earth’s surface and atmosphere. This
technology is now a key component of our observation strategies for environmental and climate monitoring, and
it is seen as a reasonable option to provide constraints on pollutant emissions for air quality control in the coming
decades. One of the key benefits of satellite remote sensing is their spatial coverage, which comes usually at the
cost of a poorer spatial and or temporal resolution. One possibility to increase the temporal resolution of Earth
observing satellites is to reduce drastically their size and therefore the cost of satellites, and launch large fleets of
identical instruments on low altitude orbits. Nano-satellites (1-50kg, standardized under the CubeSat program) are
meant to cover that need. Among the several remote sensing systems, this PhD thesis focuses on hyperspectral
imagery (imaging spectroscopy) [1], [2] which is a passive technology allowing to finely characterize the spectral
properties of objects in a scene by means of several (hundreds to thousands) narrow band spectral acquisitions.
(a) (b)
Figure 1: (a) Principle of acquisition; (b) Example of a radiance spectrum and the related interferogram (this
will be acquired by the sensor).
Some recent space programs from space agencies and spatial centers have already started to focus into
platform miniaturization and this is expected to be a leading trend in the coming years. One successful ex-
ample of miniaturized hyperspectral imaging sensors is the miniaturized Fourier transform hyperspectral cam-
era developed at the University Grenoble Alpes (IPAG laboratory and the University Space Center of Grenoble
(CSUG)) which is a partner in the framework of this PhD. The camera (which takes the volume of a matches
box) operates in the visible and near-infrared spectrum. The principle of acquisition (Figure 1a) relies on an
interferometric plate which is like a Fizeau interferometer or a low finesse Fabry Perot filter, with only two
main waves (the direct one, in solid line, and the second one, in dashed line, which has performed a round
trip in the cavity). These two waves are eventually focused on the focal plane array. Interferometric (Fourier
transform based) hyperspectral technology allows to perform acquisitions in very narrow spectral bands (around
1-2nm) in contrast to conventional hyperspectral cameras which are based on dispersive optics (with typical
5-10 nm of spectral bandwidth). This high spectral resolution is particularly needed for detecting narrow ab-
sorption/emission rays and making it the main technology for gas detection and monitoring. However, this
technology does not acquire directly a spectrum (radiance vs wave-length/-number) but a portion of an in-
terferogram which is related to the corresponding spectrum via a Fourier transform (see Figure 1b). Thus,
raw acquisitions should undergo a signal processing stage in order to provide exploitable data for applications.
This PhD will address the development of techniques for signal and image processing applied to data acqui-
sitions provided by a snapshot camera (based on the principle shown in Figure1) but operating in the ultraviolet
(UV) and visible domain which is more adapted for air quality monitoring. A prototype of the miniaturized UV
interferometric hyperspectral camera is shown in Figure 2a. An example of simulated acquisition clearly show-
ing the interferometric fringes that are superimposed on the scene. Each image corresponds to an acquisition
corresponding to a specific optical path difference (thickness of the interferometric plate). The acquired images
from the camera are composed of 400 sub-images each reproducing interferometric fringes superimposed to the
scene. The sub-images are associated to different thicknesses of the interferometric plate (thus different optical
path differences) and hence will show different patterns in the fringes (see Figure 2b). The resulting instrument
(a)
(b)
Figure 2: (a) Prototype of the camera operating in the UV-VIS domain; (b) Example of acquisition.
is expected to have wide potential applications, in particular for atmospheric monitoring of key species for air
quality and atmospheric chemistry (e.g., estimating concentrations of SO2, NO2, O3, particles, CHOCHO, BrO,
etc).
This PhD will be carried out in the collaborative framework of European (H2020) and national projects (partners
Airbus, Thales Alenia Space, Total, etc). The camera and developed software will be considered for equipping the
nanosatellite ATISE for the monitoring of the atmosphere currently under development at the CSUG1 (expected
launch in 2021). This will be a proof of concept for larger Earth observation missions for atmosphere monitoring.
Objectives of the PhD This PhD is devoted to the developments of signal and image processing techniques
applied to images acquired by the interferometric hyperspectral camera presented in Figure 2a. The main
objective will be the retrieval of spectral signatures from the raw acquisitions. Conventional algorithms for
spectral reconstruction (e.g., based on a conventional Fourier transform) are in many cases not adapted for the
processing of these acquisitions due to limiting assumptions (e.g., linearity, uniform sampling etc). There is
thus a need to develop more adapted processing techniques which better comply with the characteristics of the
acquisitions. Promising directions could be based on non-uniform Fourier transform and estimation based on
model inversion.
2
Another goal of this PhD is the estimation of parameters of interest for atmosphere monitoring (e.g., concentration
of gases, particles, etc). Different approaches will be explored during the PhD, for example carrying out the
analysis from reconstructed spectra or directly on the acquired interferograms.
The developed techniques will be tested on the instruments with both ground and airborne acquisitions. Ground
testing will take place mainly by inter-comparison with reference instruments such as MAX-DOAS installed on
existing measurement sites or during field campaigns.
Profile We are looking for a highly motivated candidate with the following skills/competences:
• MSc degree (i.e., Master 2 or Engineering school) in signal and image processing or applied mathematics,
physics, remote sensing, machine learning etc
• Solid background in signal and image processing
• Some experience in inverse problems and optimization
• Good proficiency in English (both written and spoken)
• Good programming skills (e.g., Python, Matlab)
• Solution and application oriented, able to work in a pluridisciplinary environment
• Competences in remote sensing and optics will be a plus
Supervision and Working Environment This PhD will be developed jointly at the Grenoble Images Speech
Signals and Automatics Laboratory (GIPSA-Lab) and the Institute of Geoscience and Environment (IGE), both
located on the Campus of Saint Martin d’Hères (Grenoble), France. GIPSA-lab and IGE are internationally rec-
ognize laboratories with an established expertise in signal processing and environmental monitoring, respectively.
The PhD will be supervised by:
• Mauro Dalla Mura
Mauro Dalla Mura received the B.Sc. and M.Sc. degrees in Telecommunication Engineering from the
University of Trento, Italy, in 2005 and 2007, respectively. He obtained in 2011 a joint Ph.D. degree in
Information and Communication Technologies (Telecommunications Area) from the University of Trento,
Italy and in Electrical and Computer Engineering from the University of Iceland, Iceland. In 2011 he was a
Research fellow at Fondazione Bruno Kessler, Trento, Italy, conducting research on computer vision. He is
currently an Assistant Professor at Grenoble Institute of Technology (Grenoble INP), France since 2012. He
is conducting his research at the Grenoble Images Speech Signals and Automatics Laboratory (GIPSA-Lab).
His main research activities are in the fields of remote sensing, image processing and pattern recognition.
In particular, his interests include mathematical morphology, classification and multivariate data analysis.
Dr. Dalla Mura was the recipient of the IEEE GRSS Second Prize in the Student Paper Competition of the
2011 IEEE IGARSS 2011 and co-recipient of the Best Paper Award of the International Journal of Image
and Data Fusion for the year 2012-2013 and the Symposium Paper Award for IEEE IGARSS 2014. Dr.
Dalla Mura is the President of the IEEE GRSS French Chapter since 2016 (he previously served as Secretary
2013-2016). In 2017 the IEEE GRSS French Chapter was the recipient of the IEEE GRSS Chapter Award
and the “Chapter of the year 2017” from the IEEE French Section. He is on the Editorial Board of IEEE
Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS) since 2016.
• Didier Voisin
Didier Voisin graduated as an Engineer from Ecole Polytechnique in Paris in 1994, and received a PhD from
Université Joseph Fourier (Grenoble) in 1998, working on cloud microphysics and chemistry. He was then
a Research Fellow at the National Center for Atmospheric Research in Boulder, Colorado, where he worked
on applying Chemical Ionization Mass Spectrometry to online atmospheric nanoparticles chemical analysis
and to investigating atmospheric multiphase processes. In 2004, he joined the Université Grenoble Alpes
and the Laboratoire de Glaciologie et Géophysique de l’Environnement (now Institute for Environmental
Geosciences, IGE) as an Associate Professor. He is now a Professor at UGA and IGE, and director of
the European Research Course on Atmosphere (ERCA). His research interests include understanding and
quantifying Biosphere - Atmosphere exchanges, in particular in seasonally covered areas ; snowpack (photo)
chemistry and polar atmospheric chemistry ; and organic aerosol sources and processes, in relation to Air
Quality issues and Global Change.
3• Jocelyn Chanussot
Jocelyn Chanussot received the M.Sc. degree in electrical engineering from the Grenoble Institute of
Technology (Grenoble INP), Grenoble, France, in 1995, and the Ph.D. degree from Savoie University,
Annecy, France, in 1998. In 1999, he was with the Geography Imagery Perception Laboratory for the
Delegation Generale de l’Armement, French National Defense Depart- ment, Arcueil, France. Since 1999,
he has been with Grenoble INP, where he was an Assistant Professor from 1999 to 2005 and an Associate
Professor from 2005 to 2007, and is currently a Professor of signal and image processing. He was a
member of the Institut Universitaire de France, Paris, France, from 2012 to 2017. Since 2013, he has been
an Adjunct Professor with the University of Iceland, Reykjavı́k, Iceland. He is conducting his research with
the Grenoble Images Speech Signal and Control Laboratory, Saint-Martin-d’Hères, France. His research
interests include image analysis, multicomponent image processing, nonlinear filtering, and data fusion
in remote sensing. Dr. Chanussot was a member of the IEEE Geoscience and Remote Sensing Society
Administrative Committee from 2009 to 2010 and in charge of membership development. He was also a
member of the Machine Learning for Signal Processing Technical Committee of the IEEE Signal Processing
Society from 2006 to 2008. He was the founding President of the IEEE Geoscience and Remote Sensing
French Chapter from 2007 to 2010. He was a recipient of the 2010 IEEE Geoscience and Remote Sensing
Society Chapter Excellence Award and a co-recipient of the Nordic Signal Processing Symposium 2006
Best Student Paper Award, the IEEE GRSS 2011 Symposium Best Paper Award, the IEEE GRSS 2012
Transactions Prize Paper Award, and the IEEE GRSS 2013 Highest Impact Paper Award. He was the
General Chair of the first IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution
in Remote Sensing (WHISPERS). He was the Chair from 2009 to 2011, the Co-Chair of the GRS Data Fusion
Technical Committee from 2005 to 2008, and the Program Chair of the IEEE International Workshop on
Machine Learning for Signal Processing in 2009. He was an Associate Editor of the IEEE G EOSCIENCE
AND R EMOTE S ENSING L ETTERS from 2005 to 2007 and the Pattern Recognition from 2006 to
2008. He was the Editor-in-Chief of the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH
OBSERVATIONS AND REMOTE SENSING from 2011 to 2015. Since 2007, he has been an Associate
Editor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. He was a Guest Editor
of the PROCEEDINGS OF THE IEEE in 2013 and the IEEE SIGNAL PROCESSING MAGAZINE in 2014.
The expected gross salary is around 30Keuro per year (funds covering the PhD grant are already available).
The application is open until the position is filled. The PhD thesis is expected to start in October 2018.
How to apply Interested candidates should send the following documents:
1. Curriculum vitae
2. Transcript of records (list of exams with marks and possibly a ranking with other students)
3. Motivation letter
The application should be sent by email to mauro.dalla-mura@gipsa-lab.grenoble-inp.fr, didier.voisin@univ-
grenoble-alpes.fr and jocelyn.chanussot@gipsa-lab.grenoble-inp.fr.
References
[1] M. Eismann, Hyperspectral Remote Sensing (SPIE Press Book). 2012.
[2] D. Manolakis, R. Lockwood, and T. Cooley, Hyperspectral Imaging Remote Sensing: Physics, Sensors, and
Algorithms. Cambridge University Press, 2016.
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