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Urban analysis and monitoring with multi-
temporal data: challenges and trends
F. Tupin
Télécom ParisTech - LTCI
MultiTemp 2015
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Context
Urban areas:
• 54 % of the world population
• 3.9 billions people
Urban analysis and monitoring:
• Urban mapping (urban classification, ecological impact study)
• Urban monitoring (urban growth, building / ground deformation,
subsidence,…)
• Pollution measurement
• Rapid mapping (building and network damage assesment, …)
page 1
Sao Paulo (wikipedia)
Remote sensing: global coverage of urban areas
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Overview
Remote sensing data for urban area
analysis and monitoring
State of the art and challenges for urban
areas
Advanced methods to face new needs
page 2
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Remote sensing for urban areas
page 3
Passive sensors
- optic domain
- IR domain
Active sensors
- Radar
- LiDAR
Space sensors
Airborne sensors
Ground-based sensors
Source figure ENVCAL
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HYPERION
200 bands
30m
Passive sensors – resolutions
page 4
Spatial
resolution (m)
Spectral resolution
(nb of wavelengths)
SPOT 5-6
(4bands, 6m)
Ikonos
(4bands, 4m)
Pleiades
(4bands, 2m)
MERIS
15 bands, 300m
MODIS
36 bands,
250m-1km
Time resolution
(days)
20m
Worldview 2-3
8 bands, 1.2m
8 bands SWIR
3.7m
40m 60m 100m
ASTER
14 bands
30m
- Panchromatic band with improved resolution
- Different acquisition modes (stereo / swath modes)
Landsat 8(ETM)
8bands, 30m
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HYPERION
200 bands
30m
Passive sensors – resolutions
page 5
Spatial
resolution (m)
Spectral resolution
(nb of wavelengths)
SPOT 5-6
(4bands, 6m)
Ikonos
(4bands, 4m)
Pleiades
(4bands, 2m)
MERIS
15 bands, 300m
MODIS
36 bands,
250m-1km
Time resolution
(days)
20m
Worldview 2-3
8 bands, 1.2m
8 bands SWIR
3.7m
40m 60m 100m
ASTER
14 bands
30m
- Panchromatic band with improved resolution
- Different acquisition modes (stereo / swath modes)
Landsat 8(ETM)
8bands, 30m
Sentinel 2
4 bands,10m
6 bands,20m
3 bands, 60m
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Passive sensors – revisiting time
Angular agility
Sensor constellations
• Sentinel (2), Pleiades (2), Worldview (3), …
page 6
Pleiades©CNES
Allowed angular
variation
1 sensor
(days)
2 sensors
(days)
6° 26 13
20° 7 5
30° 5 4
46° 2 1
47° 1 1
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Pleiades - time
7
Pleiades©CNES
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Remote sensing for urban areas
page 8
Active sensors
Radar (SAR)
- All time
- All weather
- Coherent imagery
- Interferometry
- Polarimetry
Source figure ENVCAL
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Alos-2 / PALSAR
(3m – full polar)
Active sensors – resolutions
page 9
Spatial
resolution (m)
Polarimetric resolution
CosmoSkyMed
(1-3m)
TerraSAR-X
(1-3m)
Time resolution
(days)
5m
Radarsat-2
(3m – full polar)
10m 20m 30m
Spotlight Stripmap ScanSAR
©Airbus D&S
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Alos-2 / PALSAR
(3m – full polar)
Active sensors – resolutions
page 10
Spatial
resolution (m)
Polarimetric resolution
CosmoSkyMed
(1-3m)
TerraSAR-X
(1-3m)
Time resolution
(days)
5m
Radarsat-2
(3m – full polar)
10m 20m 30m
Spotlight Stripmap ScanSAR
©ESA
Sentinel 1
Dual-pol
(5m-20m)
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Active sensors – revisiting time
page 11 Figure from http://www.treuropa.com
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page 12
Resolution and urban areas
Paris - Landsat image (30m)
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page 13
Spot image (10m)
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page 14
Paris : de Landsat à Orbview
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page 15 page 15
Quickbird (0.60 m), 2001
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page 16
Paris : Pléiades, 17/01/2012
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page 17
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Resolution and urban areas
Paris : ERS (descending) 1991 (12.5m)
18
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Paris: Radarsat-2 (ascending) 2005 (6m)
19
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Paris: Terrasar-X Spotlight 2007 (1m)
ascending pass
20
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page 21
TerraSAR-X
DLR project
LAN 176
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Terrasar-X SpotLight 2007 (1m)
Temporal multi-looking
22
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Urban areas: optic / SAR
page 23
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page 24
DLR
DLR
Urban areas: optic / SAR
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Multi-spectral
+ Object geometry
+ limited noise
+ « easy » to interpret
- strong influence of
illumination / atm.
conditions
- clouds
SAR
+ all time / all weather
+ high control of
acquisition geometry
+ phase information
- speckle noise
- Strong influence of
object geometry /
incidence angle
- « Difficult » to interpret
page 25
Multi-spectral vs SAR sensors
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Remote sensing data: a big data challenge
page 26
Velocity
Rapid mapping,
moving targets,…
Volume
Huge amount of
data (size, number
of channels,…)
Variety
Multi-sensors, multi
angles, multi-
wavelengths, multi-
resolution,…
Urban analysis and monitoring
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Overview
Remote sensing data for urban area
analysis and monitoring
State of the art and challenges for urban
areas
Advanced methods to face new needs
page 27
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Challenges for urban areas
page 28
Urban mapping
(classification, DEM, …)
Building / ground movement
monitoring
Urban monitoring
(temporal dynamics, urban
growth, change detection, rapid
mapping, …)
Environmental challenges
(pollution watch, ecological
impact of urban growth,…)
Different scales of analysis (local / regional / global)
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Challenges for urban areas - environment
page 29
Environmental challenges
(pollution watch, ecological
impact of urban growth,…)
IASI – CNES - EUMETSAT
TROPOMI – Sentinel 5
Regional / global scale
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Challenges for urban areas - mapping
2D classification
• Multi/hyper spectral
classification approaches
• SAR polarimetric
classification methods
[Weissberger et al. 2015]*
• SAR / optical classification
methods
• Mono-polarization SAR
(regional scale)
page 30
Urban mapping
(classification, DEM, …)
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Multi-spectral classification
page 31
Quickbird satellite image Spatio-spectral classification with MM
[Tuia et al. 2010]
© DigitalGlobe ©[Tuia et al 2010]
Classification of multi-temporal data [Demir at al. 2013] [Tuia et al. 2015]
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SAR classification
page 32
CSK image – Stripmap (2.5m resolution) Hierarchical MRF classification
[Voisin et al. 2013]
©[Voisin et al 2013] ©ASI
Local scale: limited to simple cases (isolated and specific shape buildings)
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Challenges for urban areas - 3D mapping
3D
• Stereo or multi-stereo optic
• Multi-temporal SAR
interferometry (PS, multi-
baseline, multi-aspect…)
• SAR tomography
[Porfiri et al. 2015]*
page 33
Urban mapping
(classification, DEM, …)
Backward/forward stereo acquisition
of SPOT-5
©CNES
©DLR
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Multi stereo – optic
page 34
3D point clouds generated from Pleiades tri-stereo datasets
s2p pipeline available on line [De Franchis et al. 2014] / IPOL
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Multi-baseline InSAR
page 35
Non-local TV
regularization
(3 CSK data)
[Ferraioli et al. 2015]
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SAR tomography
page 36
Las Vegas reconstruction using
tomography © DLR
[Zhu et al. 2014]
24 TerraSAR-X images
Naples stadium reconstruction using
tomography © ASI - IREA
[Fornaro et al. 2009, 2014]
29 CSK images
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Challenges for urban areas - deformation
page 37
Building / ground movement
monitoring
Building / ground deformation
• Multi-temporal SAR
interferometry (PS, multi-
baseline,…)
• 4D SAR tomography
[Zhu et al. 2009] © DLR
P. Lopez-Quiroz [Yan et al. 2012]
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Challenges for urban areas - monitoring
Change detection
Exploitation of time series (mono-sensor)
Exploitation of multi-temporal mono- or multi-
sensor images
page 38
Urban monitoring
(change detection, rapid
mapping, temporal dynamics,
urban growth, …)
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Change detection: still a challenging task!
Optic / multi-spectral sensors :
• Mono-sensor: influence of illumination conditions, viewing angle
• Multi-sensors (passive): pre-processing (registration,
calibration, atm. correc. …) => invariant features, DSM, …
page 39
SIFT key-points matching for change detection [Dellinger et al. 14]
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Change detection: still a challenging task!
Optic / multi-spectral sensors :
• Mono-sensor: influence of illumination conditions, viewing angle
• Multi-sensors (passive) : pre-processing (registration,
calibration, atm. Correc. …) => invariant features, DSM, …
page 40
DSM comparison for change detection
[Guérin et al. 14]
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Change detection: still a challenging task!
SAR sensors :
• Mono-sensor: influence of incidence angle => object level
• Multi-sensors (active) : pre-processing (registration,
calibration, …) => invariant features, object level…
page 41
Object level
change detection
[Marin et al. 15]
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Change detection: still a challenging task!
SAR sensors :
• Mono-sensor: influence of incidence angle => object level
• Multi-sensors (active) : pre-processing (registration,
calibration, …) => invariant features, object level…
page 42
Change detection
based on object
appearance
[Brunner et al. 10]
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(Satellite Image) Time series analysis
SITS analysis :
• Mono-sensor
• Multi-sensors
page 43
Multi-date divergence matrix
[Atto et al. 2013]
NORCAMA likelihood ratio change matrix clustering
[Su et al. 2015]
Temporal PolSAR BTP
[Alonso-Gonzales et al. 2014]
[Alonso-Gonzales et al 2015]*
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Time series analysis
SITS analysis :
• Mono-sensor
• Multi-angles and / or multi-sensors: still a main challenge of
multi-temporal analysis
page 44
Urban element (geometric
properties –shape,3D-,
materials,…)
Sensor
- active/passive
- Incidence angles
Illumination
conditions Modifications
(apparition /
disappearance,
deformations, …)
Registration ? [Han et al. 2015]
Calibration ?
Atm. Correction ?
3D computation ?
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Orbview WorldView
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WorldView Pléiades
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WorldView QuickBird
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Terrasar-X QuickBird
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Overview
Remote sensing data for urban area
analysis and monitoring
State of the art and challenges for urban
areas
Advanced methods to face new needs
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Advanced methods
Time signal modeling
• Huge data sequences :
- dimensionality reduction
- non-stationarity modeling
Space image modeling
• Huge size images :
- Patch-based modeling (GMM, FoE, …)
- Parcimonious decompositions [Lobry et al. 2015]
- Graph-based representations [Pham et al. 2015]
- Object-level (spatial relationship modeling, knowledge based
models …)
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Advanced methods
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Learning
• Approaches with increased efficiency ?
- Deep learning
- Active learning
- Manifold alignment [Tuia et al. 2014]
Data mining approaches
• Adaptation to urban areas ?
- Group frequent Sequential Patterns [Julea et al. 2011]
- Dynamic Time Warping similarity measures [Petitjean 2012]
- Graph-based kernel comparison [Réjichi et al. 2015]
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Conclusion and perspectives
Still many challenges to be faced:
• Compression / storage
• Combination of heterogenous data
• Exploitation of past archives
Progress in many areas
• Learning
• Image and signal modeling
Towards reproducible research ?
• OTB , IPOL , open source codes
• IEEE GRSS Image Analysis and Data Fusion TC
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ESA
ESA
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References (1)
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on polarimetric SAR images, MultiTemp’15
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GRSL, 2010
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training data, IEEE Trans. on Image Processing, 2013
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transport, MultiTemp’15
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texture in hierarchical MRF model, IEEE GRSL, 2013
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tomographic techniques, MultiTemp’15
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images, IGARSS’14
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urban areas: compressive sensing-based Tomo-SAR inversion, IEEE Signal Processing
Magazine, 2014
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Fringe’09
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References (2)
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for 3D InSAR reconstruction, IGARSS’15
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Height Estimation and Monitoring of Single and Double Scatterers, IEEE TGRS, 2009
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SIFT descriptors and an a Contrario approach, IGARSS’14
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TGRS, 2015
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SAR Imagery, IEEE TGRS, 2010
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series, IEEE TGRS, 2013
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References (3)
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IEEE TGRS, 2014
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with Binary Partition Tree, MultiTemp’15
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matrix clustering, ISPRS Journal of Photogrammetry and Remote Sensing, 2015
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analysis, MultiTemp’15
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images, MultiTemp’15
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spectral image classification, IEEE JSTARS 2015
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Analysis and Interpretation, IEEE JSTARS, 2015
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