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  • We describe an algorithm for flood mapping from SAR (particulrly CSK) that uses ……
  • Among EO data we focused on those provided by SAR because ….
  • Which are the motivations of this combined use of …. Flood mapping in vegetated areas is a challenging problem that can be tackeld by simulating the radar return under flooded conditions by means …. Since defining a criterion of membership to the class of flooded pixels is a difficult task especially in the presence of vegetation, the fuzzy ogic is suitable for designing an algorithm for flood mapping because it intrinsically accounts for .. We focus on the high res. CSK images whose spatail
  • Included an image acquired under non-flooded conditions integrate, in the classification algorithm, … In both cases a relevant portion of the flooded areas were covered by vegetation
  • Dilation adds pixels to the boundaries of objects in an image, while erosion removes pixels on object boundaries. The number of pixels added or removed from the objects in an image depends on the size and shape of the structuring element used to process the image. Both the opening and the closing operators have the effect of smoothing the image, with the opening operation removing pixels, and the closing operation adding pixels The closing operator smooths contours, links neighboring features, and fills small gaps or holes. The opening operation removes noise from an image while maintaining the sizes of objects in the foreground. Opening is a useful process for smoothing contours, removing pixel noise, eliminating narrow extensions, and breaking thin links between features. The function of opening is to dilate an eroded image in order to recover as much as possible of the original image. On the contrary, the function of closing is to erode a dilated image in order to recover the initial shape of image structures that have been dilated
  • Parameters of the fuzzy funcions are the fuzzy thresholds
  • This the radar return simulated by the selected EM model under dry conditions (for different smc) and under flooded conditions
  • For a 70 cm tall wheat plant this is the increase of radar return predicted by the model (4 dB) so that we can use the S function and if the increase of sigma0 is larger than 4 dB the degree of mebership to the class of flooded areas is 1. To evaluate this increase we need a SAR image under dry conditions
  • For bare soils the increase is due to the transition from a flooded soil to a very humid sol (SMC around saturation)
  • For vegetated soils we can rely again on em simulations If the emerging part of the plant is small the scattering center moves toward the upper part of the stem and the above vegetation attenuating layer becomes thinner
  • To account fo the previous considerations (outcomes of the simulations) we can use the fuzzy logic
  • We do not have any ground truth data but we went in the Tamaro river area one month after the flood to get information about this event so that we know that these areas were actually flooded
  • Pulvirenti_IGARSS2011.ppt

    1. 1. Combined use of Electromagnetic Scattering Models, Fuzzy Logic and Mathematical Morphology for Flood Mapping from COSMO-SkyMED data L. Pulvirenti 1 , M. Chini 2 , N. Pierdicca 1 , L. Guerriero 3 (1) Sapienza, University of Rome (2) Istituto Nazionale di Geofisica e Vulcanologia (3) Tor Vergata University of Rome
    2. 2. Introduction <ul><li>Several overflows occurred in Italy in the recent years </li></ul><ul><ul><li>ASI is presently funding some investigations about the use of Earth Observation data for civil protection from floods (e.g., OPERA ). </li></ul></ul><ul><li>Potential of SAR for flood monitoring </li></ul><ul><ul><li>The synoptic view , the good spatial resolution , the capabilities to operate in almost all-weather conditions and both during daytime and nighttime are the key features of radar sensors. </li></ul></ul><ul><li>Possible advantages of using X-band COSMO-SkyMED images : </li></ul><ul><ul><li>Very high spatial resolution (especially in the spotlight configuration) -> An accurate flood boundary delineation can be expected. </li></ul></ul><ul><ul><li>Short revisit time (constellation of 4 satellites) -> A Multi-temporal analysis can be performed. </li></ul></ul>
    3. 3. Motivations <ul><li>SAR data interpretation often not straightforward, especially in the presence of vegetation ( challenging problem ). </li></ul><ul><ul><li>Need to rely on electromagnetic scattering models , simulating  0 under flooded conditions, to correctly interpret the SAR observations. </li></ul></ul><ul><li>Fuzzy logic suitable for representing the set of flooded pixels in SAR images for which the definition of a criterion of membership is a difficult task. </li></ul><ul><li>Spatial details of high resolution images generally smaller than the dimensions of the targets -> large within-class variances (also because of the speckle noise) </li></ul><ul><ul><li>Need to segment imagery for dealing with homogeneous areas. </li></ul></ul><ul><ul><li>Mathematical morphology allows identifying objects with different spatial extension (when used in a multi-scale manner). </li></ul></ul>
    4. 4. Steps of the designed algorithm and case studies <ul><li>Segmentation of a multi-temporal series of CSK observations of a flood. </li></ul><ul><li>Computation of the average  0 for each segment. </li></ul><ul><li>Application to the segmented images of the fuzzy-logic-based approach </li></ul><ul><ul><li>allowing us to account for different scattering mechanisms. </li></ul></ul><ul><li>Methodology tested on two case studies (OPERA team activated) : </li></ul><ul><ul><li>the overflow of the Tanaro River, close to the city of Alessandria (Northern Italy) in April 2009 ( 4 CSK images used ). </li></ul></ul><ul><ul><li>the flood occurred in Tuscany (central Italy) in December 2009 near the Massaciuccoli lake ( 5 CSK images used ). </li></ul></ul><ul><ul><li>In both cases a portion of flooded area was vegetated. </li></ul></ul>
    5. 5. <ul><li>The opening (erosion followed by dilation) and closing morphological operators are applied with structuring elements ( se ) of different sizes. </li></ul><ul><ul><li>Structures in a SAR image may have a high response for a specific selected se size and a lower response for other sizes . </li></ul></ul><ul><li>The morphological profile is built for each image of the available multi-temporal series. </li></ul><ul><li>A K-means clustering is applied to the multi-temporal profile. </li></ul><ul><li>The final segmentation (extraction of contiguous objects belonging to the same class) is performed. </li></ul>Segmentation N
    6. 6. The fuzzy sets <ul><li>Degree of membership to a fuzzy set defined through the standard S and Z functions. </li></ul>fuzzy thresholds <ul><li>Default values of the fuzzy thresholds based on the outputs of the EM scattering model developed at the Tor Vergata University of Rome. It assumes: </li></ul><ul><ul><li>Bare soil : IEM </li></ul></ul><ul><li>Vegetation : homogeneous half space overlaid by a layer filled with discrete dielectric scatterers representing stems and leaves. </li></ul><ul><li>Flooded conditions : simulated substituting the soil with a semi-infinite layer having the  of water and a negligible roughness. </li></ul>S function 0 1 0 1 Z function
    7. 7. Fuzzy set of flooded bare soils <ul><li>Flooded bare soils generally much smoother than the surrounding dry land, thus acting as specular reflectors, giving low  0 . </li></ul>Flooded  0 1 X Band, HH pol  0 HH SMC [%]
    8. 8. Set of vegetated flooded areas <ul><li>Protruding vegetation may produce large  0 . </li></ul><ul><li>Reflections between water surface and upright vegetation may enhance backscattering -> flooded vegetation may show a bright radar return in a SAR image. </li></ul>0 1  wheat plant height=70 cm  0 HH SMC [%]
    9. 9. Multitemporal analysis Tanaro river overflow . RGB color composite of the CSK observations of the flood Red : April, 29 2009 Green : April, 30 2009 Blue : May, 1 2009 NDVI map ( AVNIR-2 image acquired on April 23, 2009 ) bare vege t ated vegetated Mean (  0 ) [dB] April, 29 April, 30 May, 1 May, 16 dry
    10. 10. Dependence of  0 on water level <ul><li>Radar return predicted by the EM model (small leaves) versus the water level ( h w ) . </li></ul>Large double buonce effect Small double buonce effect h w > h 60 cm plant 25cm plant 75 cm plant  0 [dB] h w [cm]
    11. 11. Other fuzzy rules (multitemporal analysis) <ul><li>Non-flooded objects at time t are generally non-flooded at time t+ 1 </li></ul><ul><li>Flooded objects that at time t have small  0 may be flooded at time t+ 1 if  0 ( t+ 1 ) considerably larger than  0 ( t ) (decrease of h w ) </li></ul><ul><li>Flooded objects that at time t have large  0 may be flooded at time t+ 1 if  0 ( t+ 1 ) <  0 ( t ) (decrease of h w ) </li></ul><ul><li>Non-flooded objects surronded by flooded ones placed at higher altitude are probably flooded (DEM-based correction) </li></ul>60 cm plant 25cm plant 75 cm plant  0 [dB] h w [cm] 0 1
    12. 12. The Tanaro overflow <ul><li>Occurred near the town of Alessandria (Northern Italy) on April 27-28, 2009. </li></ul><ul><li>~ 6000 people were evacuated for precaution. </li></ul><ul><li>Some agricultural fields were inundated. These fields were either bare or covered by wheat at different stage of growth (early – intermediate) . </li></ul>
    13. 13. Segmentation results Tanaro flood: original images RGB color composite (3500x5000 pixels) Red : April 29, 2009 Green : April 30, 2009 Blue : May 1, 2009 Tanaro flood: segmented image ~ 8000 objects
    14. 14. Flood evolution map (Apr. 29- May 1) Cyan :flooded Blue: water bodies
    15. 15. Flood map of April 29, 2009
    16. 16. The Tuscany flood <ul><li>Occurred near the Massaciuccoli lake (Central Italy) on December 25-26, 2009. </li></ul>CSK database Acquisition time Incidence angle Orbit Mode Pol. Dec. 20, 2009 30.6° RA Stripmap HH Dec. 30, 2009 32.1° LD Stripmap HH Dec. 31, 2009 43.9° RD Stripmap HH Jan. 01, 2010 24.1° RD Stripmap HH Jan. 04, 2010 29.1° RA Stripmap HH
    17. 17. Segmentation results Tuscany flood: original image Tuscany flood: segmented image ~ 3700 objects (codes represented in grayscale) RGB color composite (3000x1500 pixels) Red : December, 20 2009 Green : December, 30 2009 Blue : December, 31 2009
    18. 18. A distinctive multi-temporal signature Dec. 27, 2009 dry
    19. 19. Flood evolution map Cyan :flooded Blue: water bodies
    20. 20. Conclusions <ul><li>The COSMO-SkyMed mission offers a unique opportunity to obtain radar images characterized by short revisit time </li></ul><ul><ul><li>Potential usefulness for monitoring the temporal evolution of floods. </li></ul></ul><ul><li>A combined approach using an advanced segmentation technique and a well-established surface scattering model has been presented </li></ul><ul><li>The objects with distinctive multi-temporal trends have been identified by the segmentation algorithm. </li></ul><ul><li>Simulations has allowed us to explain COSMO-SkyMed multi-temporal signatures of different surface types (vegetated or bare). </li></ul><ul><li>This work has been supported by the Italian Space Agency (ASI) under contract No. I/048/07/0. </li></ul>