URBAN AREA PRODUCT
SIMULATION FOR THE
ENMAP HYPERSPECTRAL
SENSOR
P.Gamba, A. Villa, A. Plaza,
J. Chanussot, J. A. Benediktsson
OUTLINE
1)
2)
3)
4)
Introduction
The EnMap sensor
Experimental test
Conclusions and perspectives
INTRODUCTION
Urban remote sensing is a crucial task :
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•
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
Monitoring cities development
Infrastructure extraction
Land cover mapping
Satellites provide cheap and timely data
Main challenges :
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Urban land cover classes not well distinct
Heterogeneous areas lead to mixed pixels
Can hyperspectral data be helpul for such a task?
INTRODUCTION
Can hyperspectral data be helpul for urban monitoring?
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Pro: very detailed spectral information of the scene
Con: tradeoff between spectral and spatial resolution
(~tens of meters)
A number of hyperspectral missions already planned:
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PRISMA – ASI, 2014 (spatial res: 30 m)
EnMap – DLR, 2014 (spatial res: 30 m)
Hyper-J (JAXA) and HyspIRI (NASA) in the next future..
Need to assess the effectiveness of hyperspectral
data for urban monitoring!
AIM OF THE WORK
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Preliminary investigation of hyperspectral sensors potentialities:
- Can urban structures be monitored with hyperspectral sensors?
- Is the rich spectral information of hyperspectral data useful for such a task?
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Two main issues addressed:
1) Creation of realistic urban scenes by considering EnMap PSF
2) Comparison with classical and advanced methods
OUTLINE
1)
2)
3)
4)
Introduction
The EnMap sensor
Experimental test
Conclusions and perspectives
EnMap mission

Dedicated imaging pushbroom hyperspectral sensor mainly based on modified
existing or pre-developed technology

Broad spectral range from 420 nm to 1000 nm (VNIR) and from 900 nm to 2450 nm
(SWIR) with high radiometric resolution and stability in both spectral ranges

Swath width 30km at high spatial resolution of 30 m x 30 m and off-nadir (30°)
pointing feature for fast target revisit (4 days)

Sufficient on-board memory to acquire 1.000 km swath length per orbit and a total of
5.000 km per day.
S. Kaiser , B. Sang , J. Schubert , S. Hofer and T. Stuffler: "Compact prism spectrometer of
pushbroom type for hyperspectral imaging", Proc. SPIE Conf. Imaging Spectrometry XIII, vol.
7100, p.710001, 2008.
SYNTHETIC REALISTIC IMAGES
1) Create realistic hyperspectral images by downscaling the spatial
resolution according to the EnMap PSF
EnMap PSF (900 nm)
Spatial resolution
degradation
Original image
Low resolution image
CLASSIFICATION COMPARISON
2) Evaluate the performances of traditional methods and sub-pixel
techniques in terms of land cover classification
SVM
SVM-SU1
Classification
map
Classification
map at finer
resolution
Low resolution image
1.
Villa et al., Spectral Unmixing to obtain classification maps at a finer resolution, Journal of Selected
Topics in Signal Processing, 2011
SVM - SU
SVM – Spectral Unmixing(*):
(*)A.
1)
Probabilistic SVM determines which
pixels can be considered as pure (if
prob > treshold)
2)
Spectral unmixing is used to retrieve
class abundances within mixed pixels
3)
Final spatial regularization
Villa, J. Chanussot, J.A. Benediktsson and C. Jutten., Spectral Unmixing to obtain
classification maps at a finer resolution, Journal of Selected Topics in Signal Processing, vol. 5,
n. 3, May 2011
SVM - SU
SVM – Spectral Unmixing:
1)
Probabilistic SVM determines which
pixels can be considered as pure (if
prob > treshold)
2)
Spectral unmixing is used to retrieve
class abundances within mixed pixels
and to fill “upsampled” sub-pixels
3)
Final spatial regularization
SVM - SU
SVM – Spectral Unmixing:
1)
Probabilistic SVM determines which pixels
can be considered as pure (if
prob > threshold)
2)
Spectral unmixing is used to retrieve class
abundances within mixed pixels
and to fill “upsampled” sub-pixels
3)
Final spatial regularization is performed
(Cost function to be minimized: total
perimeter of the connected areas )
The results is a thematic map at a finer resolution  useful to assess
possibilities offered by HSI at low-medium spatial resolution.
OUTLINE
1)
2)
3)
4)
Introduction
The EnMap sensor
Experimental test
Conclusions and perspectives
DATA SET
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ROSIS Center data set:
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1096 x 712 pixels, 102 spectral bands
Spatial resolution: 1.3 meters
Spatial resolution decreased of a factor 3 and 5
Classification with SVM and SVM-SU, 100 samples per class.
EXPERIMENTS
Reference
data
SVM
SVM-SU
ROSIS Center (3x downscale)
OA (%)
Δ (%)
98.11
79.56
81.89
- 19.55
- 16.22
ROSIS Center (5x downscale)
OA (%)
Δ (%)
98.11
70.97
74.32
- 27.14
- 23.79
THEMATIC MAPS
SVM on original HR data
(ground truth)
SVM on LR data
Finer Classification
70.97%
74.32%
THEMATIC MAPS
SVM on original HR data
(ground truth)
SVM on LR data
Finer Classification
70.97%
74.32%
EXPERIMENTS
Preliminary conclusions
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Even slight spatial resolution degradation leads to a significant classification
accuracy decrease
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Sub-pixel information of HSI is helpful to improve classification accuracy
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Potentialities of hyperspectral data depend on the desired application
OUTLINE
1)
2)
3)
4)
Introduction
The EnMap sensor
Experimental test
Conclusions and perspectives
CONCLUSIONS
Conclusions
--
Low spatial resolution is a major challenge in urban environment
→ Hard to perform urban monitoring with hyperspectral sensors
--
Additional spectral information useful if properly exploited
→
Need for advanced techniques to study sub-pixel information
Perspectives
- Consider more realistic models (more PSFs, geometric distortions..)
- Investigate lower spatial resolution (comparable to HSI)
- Further investigations of the possibilities offered by sub-pixel methods