Integration of Colorized Single-Pol SAR Data Into Optical Image Mosaics Oliver Lang Parivash Lumsdon Astrium  GEO-Informat...
Motivation <ul><li>Thematic mapping in Cloud Belt using single-pol SAR </li></ul><ul><li>Cost effective approach: single c...
Single-Pol SAR Colorization <ul><li>Known: Basic „classification“ based on Speckle variations </li></ul><ul><li>Coeff. of ...
General  Approach <ul><li>Apply multiscale texture filters </li></ul><ul><li>Classification based on filter layers </li></...
Generation of filter layers <ul><li>Derivation of multi-scale texture components </li></ul><ul><ul><li>Mean </li></ul></ul...
Classification <ul><li>Hierarchical unsupervised classification based on filter layers </li></ul><ul><li>Min-distance base...
Selection of Colors <ul><li>2 methods: </li></ul><ul><ul><li>Predefined standard color tables </li></ul></ul><ul><ul><li>(...
2 Examples <ul><li>Overlay: Spot 4 and TerraSAR-X Stripmap </li></ul><ul><li>Overlay: TerraSAR-X Spotlight in Google Earth...
Example: Cameroon TerraSAR-X: Date: 29 Jul 2010 StripMap, 3 m res  HH polarization   SPOT4 :  date: 8 Jan 2011 20 m resolu...
<ul><li>Color tables derived from overlapping optical scene </li></ul><ul><li>Nr. of quantized colors: 16 </li></ul>Exampl...
Example: Germany <ul><li>Quantization:  256 colors / class </li></ul>Germany: TerraSAR-X HS  urban forest agriculture water
Example: Germany Background image: Google Earth  Germany: TerraSAR-X HS  urban forest agriculture water
Discussion <ul><li>Sensor and SAR-mode independent qualitative approach </li></ul><ul><li>Supports thematic mapping as add...
Thank You Astana, Kasachstan TerraSAR-X StripMap Color SAR
Contact <ul><li>Dr. Oliver Lang </li></ul><ul><li>Senior Application Development Manager </li></ul><ul><li>Development & E...
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Integration of Colorized Single-Pol SAR Data Into Optical Image Mosaics.ppt

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  • SatDSig Hinweis: Die Bilddimensionen betragen: 14550m (Ost-West), 8790m (Nord-Süd) beziehungsweise 28187 Pixel x 17176 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
  • Integration of Colorized Single-Pol SAR Data Into Optical Image Mosaics.ppt

    1. 1. Integration of Colorized Single-Pol SAR Data Into Optical Image Mosaics Oliver Lang Parivash Lumsdon Astrium GEO-Information Services IGARSS 2011, Vancouver
    2. 2. Motivation <ul><li>Thematic mapping in Cloud Belt using single-pol SAR </li></ul><ul><li>Cost effective approach: single coverage, full resolution + swath width </li></ul><ul><li>Mosaic electro-optical image mosaics with seamless SAR mosaics, colorized in meaningful way </li></ul><ul><li>Commercial TerraSAR-X data distribution by Astrium: Colored quick looks come with TSX data since 2010 BUT: varying colors, not intuitive </li></ul><ul><li> Development of new add-on product: Color Composite </li></ul>
    3. 3. Single-Pol SAR Colorization <ul><li>Known: Basic „classification“ based on Speckle variations </li></ul><ul><li>Coeff. of Variation as measure for local speckle noise </li></ul><ul><li>Link to main surface types: </li></ul><ul><ul><li>Large CoV: heterogenious (urban) </li></ul></ul><ul><ul><li>Small CoV: homogenious (water, grassland) </li></ul></ul><ul><li>New: </li></ul><ul><ul><li>combination of multiple texture filters </li></ul></ul><ul><ul><li>Colorization according to reference image </li></ul></ul>S. Kuntz and F. Siegert, “Monitoring of deforestation and land use in Indonesia with multitemporal ERS data.” International Journal of Remote Sensing 20: 2835-2853, 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 STD mean
    4. 4. General Approach <ul><li>Apply multiscale texture filters </li></ul><ul><li>Classification based on filter layers </li></ul><ul><li>Colorization of „classes“ with given LUTs </li></ul>
    5. 5. Generation of filter layers <ul><li>Derivation of multi-scale texture components </li></ul><ul><ul><li>Mean </li></ul></ul><ul><ul><li>Standard Deviation </li></ul></ul><ul><ul><li>Variance </li></ul></ul><ul><ul><li>Skewness </li></ul></ul><ul><ul><li> Coeff of Variation </li></ul></ul><ul><li>Spectral high-pass </li></ul><ul><li>Noise components: </li></ul><ul><ul><li>Multiplicative Noise S : apply Gaussian filter </li></ul></ul><ul><ul><li>Additive Noise N = apply directional Lee filtered </li></ul></ul>
    6. 6. Classification <ul><li>Hierarchical unsupervised classification based on filter layers </li></ul><ul><li>Min-distance based on empirical thresholds </li></ul><ul><li>Backscatter & speckle characteristics allows reliable separaton of (calm) Water / Urban </li></ul><ul><li>Third class is separated into hetero- and honogenious sub-class (e.g. Forest / Grassland ) </li></ul><ul><ul><li>Decider: local Variance </li></ul></ul>Histogram value Urban Forest Water 0 50 100 150 200 250 mean STD
    7. 7. Selection of Colors <ul><li>2 methods: </li></ul><ul><ul><li>Predefined standard color tables </li></ul></ul><ul><ul><li>(optical) reference image </li></ul></ul><ul><li>Manual or automatic selection of samples for each class </li></ul><ul><li>Derivation of Hue values from samples and quantization of colors to a desired number of colors  4 LUTs </li></ul><ul><li>HSV  RGB Transformation </li></ul>Example: selection of sample areas Background image: Google Earth mean STD hue Saturation
    8. 8. 2 Examples <ul><li>Overlay: Spot 4 and TerraSAR-X Stripmap </li></ul><ul><li>Overlay: TerraSAR-X Spotlight in Google Earth </li></ul>
    9. 9. Example: Cameroon TerraSAR-X: Date: 29 Jul 2010 StripMap, 3 m res HH polarization SPOT4 : date: 8 Jan 2011 20 m resolution, Layers 4, 1, 2 10 km
    10. 10. <ul><li>Color tables derived from overlapping optical scene </li></ul><ul><li>Nr. of quantized colors: 16 </li></ul>Example: Cameroon TerraSAR-X: Date: 29 Jul 2010 StripMap, 3 m res HH polarization SPOT4 : date: 8 Jan 201 20 m resolution, Layers 4, 1, 2 Water Agriculture Forest Urban 10 km
    11. 11. Example: Germany <ul><li>Quantization: 256 colors / class </li></ul>Germany: TerraSAR-X HS urban forest agriculture water
    12. 12. Example: Germany Background image: Google Earth Germany: TerraSAR-X HS urban forest agriculture water
    13. 13. Discussion <ul><li>Sensor and SAR-mode independent qualitative approach </li></ul><ul><li>Supports thematic mapping as additional information layer, e.g. in cloud belt </li></ul><ul><li>Intuitive visualization and interactive interpretation </li></ul><ul><li>SAR specific backscatter characteristics remain </li></ul><ul><ul><li>Inherently, differences regarding surface representation between optical and SAR remain </li></ul></ul><ul><li>Further improvements expected by optimized classification procedure & automatic LUT derivation </li></ul>0% 100% Clouds
    14. 14. Thank You Astana, Kasachstan TerraSAR-X StripMap Color SAR
    15. 15. Contact <ul><li>Dr. Oliver Lang </li></ul><ul><li>Senior Application Development Manager </li></ul><ul><li>Development & Engineering | Infoterra GmbH </li></ul><ul><li>GEO-Information Services </li></ul>Astrium GmbH - Services Claude-Dornier-Str. | 88090 Immenstaad | Germany Tel +49 7545 8 5520 | Fax +49 7545 8 1337 | Mob +49 151 1822 0827 [email_address] | www.infoterra.de

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