1. Integration of Colorized Single-Pol SAR Data Into Optical Image Mosaics
IGARSS 2011, Vancouver
Oliver Lang Parivash Lumsdon
Astrium GEO-Information Services

2. IGARSS Vancouver – 27 July 2011
Motivation
Thematic mapping in Cloud Belt using single- pol SAR
Cost effective approach: single coverage, full resolution + swath width
Mosaic electro-optical image mosaics with seamless SAR mosaics, colorized in meaningful way
Commercial TerraSAR-X data distribution by Astrium: Colored quick looks come with TSX data since BUT: varying colors, not intuitive
 Development of new add-on product: Color Composite
IGARSS Vancouver – 27 July 2011

3. Single-Pol SAR Colorization
Known: Basic „classification“ based on Speckle variations
Coeff. of Variation as measure for local speckle noise
Link to main surface types:
Large CoV: heterogenious (urban)
Small CoV: homogenious (water, grassland)
New:
combination of multiple texture filters
Colorization according to reference image
mean
STD
S. Kuntz and F. Siegert, “Monitoring of deforestation and land use in Indonesia with multitemporal ERS data.” International Journal of Remote Sensing 20: , 1999
M. Thiel., T. Esch, and S. Dech, “Object-oriented detection of settlement areas from TerraSAR-X data” Proceedings of the EARSeL Joint Workshop: Remote Sensing: New Challenges of high resolution. (Eds.,Carsten Jürgens), 2008
IGARSS Vancouver – 27 July 2011

4. IGARSS Vancouver – 27 July 2011
General Approach
Apply multiscale texture filters
Classification based on filter layers
Colorization of „classes“ with given LUTs
IGARSS Vancouver – 27 July 2011

5. Generation of filter layers
Derivation of multi-scale texture components
Mean
Standard Deviation
Variance
Skewness
 Coeff of Variation
Spectral high-pass
Noise components:
Multiplicative Noise S: apply Gaussian filter
Additive Noise N = apply directional Lee filtered
IGARSS Vancouver – 27 July 2011

6. IGARSS Vancouver – 27 July 2011
Classification
Histogram
Urban
Forest
Water 50
100
150
200
250
Hierarchical unsupervised classification based on filter layers
Min-distance based on empirical thresholds
Backscatter & speckle characteristics allows reliable separaton of (calm) Water / Urban
Third class is separated into hetero- and honogenious sub- class (e.g. Forest / Grassland)
Decider: local Variance
value
mean
STD
IGARSS Vancouver – 27 July 2011

7. IGARSS Vancouver – 27 July 2011
Selection of Colors
Example: selection of sample areas
Background image: Google Earth
2 methods:
Predefined standard color tables
(optical) reference image
Manual or automatic selection of samples for each class
Derivation of Hue values from samples and quantization of colors to a desired number of colors  4 LUTs
HSV  RGB Transformation
mean
STD
hue
Saturation
IGARSS Vancouver – 27 July 2011

8. IGARSS Vancouver – 27 July 2011
2 Examples
Overlay: Spot 4 and TerraSAR-X Stripmap
Overlay: TerraSAR-X Spotlight in Google Earth
IGARSS Vancouver – 27 July 2011

9. IGARSS Vancouver – 27 July 2011
Example: Cameroon
SPOT4: date: 8 Jan m resolution, Layers 4, 1, 2
TerraSAR-X:
Date: 29 Jul 2010 StripMap, 3 m res HH polarization
10 km
IGARSS Vancouver – 27 July 2011

10. IGARSS Vancouver – 27 July 2011
Water
Agriculture
Forest
Urban
Example: Cameroon
Color tables derived from overlapping optical scene
Nr. of quantized colors: 16
SPOT4: date: 8 Jan m resolution, Layers 4, 1, 2
TerraSAR-X:
Date: 29 Jul 2010 StripMap, 3 m res HH polarization
10 km
IGARSS Vancouver – 27 July 2011

11. IGARSS Vancouver – 27 July 2011
Example: Germany
Quantization: colors / class
urban
forest
agriculture
water
Germany: TerraSAR-X HS
IGARSS Vancouver – 27 July 2011

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urban
forest
agriculture
water
Example: Germany
SatDSig Hinweis:
Die Bilddimensionen betragen:
14550m (Ost-West), 8790m (Nord-Süd) beziehungsweise Pixel x Pixel
dies entspricht bei einer vorgegebenen Bildschrimauflösung eine Bildgröße von 31,59 cm x 21,17cm
In PPT: 786 x 509 Pix  resolution: ca. 18m
Germany: TerraSAR-X HS
Background image: Google Earth
IGARSS Vancouver – 27 July 2011

13. IGARSS Vancouver – 27 July 2011
Discussion
Sensor and SAR-mode independent qualitative approach
Supports thematic mapping as additional information layer, e.g. in cloud belt
Intuitive visualization and interactive interpretation
SAR specific backscatter characteristics remain
Inherently, differences regarding surface representation between optical and SAR remain
Further improvements expected by optimized classification procedure & automatic LUT derivation 0%
100%
Clouds
IGARSS Vancouver – 27 July 2011

14. IGARSS Vancouver – 27 July 2011
Thank You
Astana, Kasachstan TerraSAR-X StripMap ColorSAR
IGARSS Vancouver – 27 July 2011

15. IGARSS Vancouver – 27 July 2011
Contact
Dr. Oliver Lang
Senior Application Development Manager
Development & Engineering | Infoterra GmbH
GEO-Information Services
Astrium GmbH - Services
Claude-Dornier-Str. | Immenstaad | Germany
Tel | Fax | Mob |
IGARSS Vancouver – 27 July 2011