1. IEEE Gold Remote Sensing Conference 2010 Naval Academy, Livorno, Italy, April 29-30, 2010 Flooding Maps From Cosmo-Skymed Images Elena Angiati Silvana Dellepiane University of Genoa (Italy) Dept. of Biophysical and Electronic Engineering (DIBE) NUMIP – NUMerical Image Processing

2. Outline Introduction: Identification of flooded areas; The proposed method: fast-ready flood maps  pre-processing & RGB composition; detailed flood maps segmentation approach. Experimental results: experiments on SAR images Conclusions “OPERA – Civil protection from floods” pilot project - Italian Space Agency & Italian Department for Civil Protection.

3. Introduction Multitemporal remote-sensing images represent a powerful source of information for monitoring the evolution of the Earth’s surface Relevant task: identification of flooded areas. SAR images are particularly useful during floods: all-weather capability cloud-penetrating properties

4. Fast-ready flood maps An RGB composition is used, where two images are combined into a false colour composite image  enhancing the flooded areas Images can be acquired with different sensor parameters  an appropriate pre-processing is required Three sequential steps are proposed: filtering, adaptive histogram truncation, equalization.

5. Filtering Comparison of different filters: SRAD (Speckle Reducing Anisotropic Diffusion), Lee, Frost, Enhanced Lee and Frost filters. SRAD allows to reduce noise and to preserve details. Best performances in the frequency domain  mean preservation and isotropic behavior. Original image Lee Frost Enhanced Lee Enhanced Frost SRAD

6. Histogram Equalization Linear shrinking from 2 Bytes to 1 Byte  loss of many informative contents, due to the very long distribution tail Histogram equalization  normalization of the different histogram distributions Usual histogram equalization is not properly working with such a heavy tail.  Adaptive histogram truncation is applied Zoom into the interval 0-500 of original histogram of image (maximum value = 18000) Histogram of equalized image

7. Histogram truncation & Equalization Preliminary clipping to the 95th percentile & equalization  best performances RGB composition image is obtained: Red channel: difference between pre and post-event Green: post-event image Blue: pre-event image Blue = uniformcumulative function Magenta = cumulative ofimage Adaptivehistogramequalization (truncation& equalization) Histogramequalizationoforiginalimage

8. Detailed flood maps A multi-seed-growing segmentation approach is employed. Segmentation process: uses filtered images; starts from water pixels; uses an anisotropic image-scanning mechanism  order of pixel analysis is dependent on the image content. Test rule  a similarity criterion is satisfied.

9. Segmentation algorithm Given the seed point , a “seed region” is generated, using the seed pointand its direct 8-neighbours: The sample mean is computed: Sample standard deviation is computed on a 5x5 window centered on the seed pixel:

10. Segmentation algorithm Sample mean m aggregation rule Sample standard deviation s estimate the threshold value. The threshold is adaptive to the scattering of the region of interest and is set to: A new pixel is assigned to the region if its distance with respect to the “seed region” is small enough.

11. Data set Different multitemporal data set consisting of pair of co-registered Cosmo/Skymed images are used. Flood event of the MassaciuccoliLake: images in Stripmap acquisition modes, with different geometric acquisition parameters Cosmo/SkymedStripmap images (spacial resolution: 2,5 meters) LEFT: 20th December 2009 (ascending/right looking angle) RIGHT: 30th December 2009 (descending/left looking angle)

12. Example of Fast-ready flood map The images could be used in an RGB composition despite the different acquisition parameters RGB composition. In magenta: change due to decrease of backscattering, corresponding to flooded areas. In cyan: no-change due to high backscattering in both images In bordeaux: no-change due to low backscattering in both images

13. Example of Detailed flood map The segmentation process is not affected by different acquisition setting  the filtered images can be used. Detailed map of flooded areas. In blue: steadywater In cyan: flooded areas

14. Otherexamples on Stripmapimages Cosmo/Skymed images acquired near Scutari (Albania) in Stripmap mode (spatial resolution: 2,5 meters), with different acquisition parameters 10th January 2010 - in descending configuration with right look angle 15th January 2010 - in ascending configuration with right look angle Fast-readyfloodmap Detailedfloodmap Floodedareas Floodedareas Steady water Steady water No floodedareas Otherchanges

15. Otherexamples on Stripmapimages Cosmo/Skymed images acquired near Alessandria (Italy) in Stripmap mode (pixel resolution: 2,5 meters), in descending configuration withright look angle 30th April 2009 1st May 2009 Detailedfloodmap Fast-readyfloodmap Floodedareas Floodedareas No floodedareas Steady water Steady water Otherchanges

16. Examples on Spotlightimages Cosmo/Skymed images acquired near Alessandria (Italy) in Spotlight mode (pixel resolution: 0,5 meters), with different acquisition parameters 1st May 2009 - in ascending configuration with right look angle 29th April 2009 - in descending configuration with left look angle 30th April 2009 - in ascending configuration with right look angle Fast-readyfloodmap Detailedfloodmap Multitemporalfloodmap Floodedareas at 29th April 2009 Floodedareas at 30th April 2009 Steady water Floodedareas No floodedareas Floodedareas Steady water Steady water Otherchanges

17. Conclusions Several image processing techniques and a segmentation method have been proposed. Images acquired by the new mission Cosmo/Skymed have been used for experiments. Both qualitative and quantitative algorithms have been presented and very good performances have been obtained in both cases.