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IEEE Gold Remote Sensing Conference 2010<br />Naval Academy, Livorno, Italy, April 29-30, 2010<br />Flooding Maps From Cos...
Outline<br />Introduction:<br />Identification of flooded areas;<br />The proposed method:<br />fast-ready flood maps  pr...
Introduction<br />Multitemporal remote-sensing images represent a powerful source of information for monitoring the evolut...
Fast-ready flood maps<br />An RGB composition is used, where two images are combined into a false colour composite image ...
Filtering<br />Comparison of different filters: SRAD (Speckle Reducing Anisotropic Diffusion), Lee, Frost, Enhanced Lee an...
Histogram Equalization<br />Linear shrinking from 2 Bytes to 1 Byte  loss of many informative contents, due to the very l...
Histogram truncation & Equalization<br />Preliminary clipping to the 95th percentile & equalization  best performances<br...
Detailed flood maps<br />A multi-seed-growing segmentation approach is employed.<br />Segmentation process:<br />uses filt...
Segmentation algorithm <br />Given the seed point           , a “seed region” is generated, using the seed pointand its di...
Segmentation algorithm<br />Sample mean m aggregation rule<br />Sample standard deviation s estimate the threshold value...
Data set<br />Different multitemporal data set consisting of pair of co-registered Cosmo/Skymed images are used. <br />Flo...
Example of Fast-ready flood map<br />The images could be used in an RGB composition despite the different acquisition para...
Example of Detailed flood map<br />The segmentation process is not affected by different acquisition setting  the filtere...
Otherexamples on Stripmapimages<br />Cosmo/Skymed images acquired near Scutari (Albania) in Stripmap mode (spatial resolut...
Otherexamples on Stripmapimages<br />Cosmo/Skymed images acquired near Alessandria (Italy) in Stripmap mode (pixel resolut...
Examples on Spotlightimages<br />Cosmo/Skymed images acquired near Alessandria (Italy) in Spotlight mode (pixel resolution...
Conclusions<br />Several image processing techniques and a segmentation method have been proposed.<br />Images acquired by...
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Ieee gold angiati

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Ieee gold angiati

  1. 1. IEEE Gold Remote Sensing Conference 2010<br />Naval Academy, Livorno, Italy, April 29-30, 2010<br />Flooding Maps From Cosmo-Skymed Images<br />Elena Angiati<br />Silvana Dellepiane<br />University of Genoa (Italy)<br />Dept. of Biophysical and Electronic Engineering (DIBE)<br />NUMIP – NUMerical Image Processing<br />
  2. 2. Outline<br />Introduction:<br />Identification of flooded areas;<br />The proposed method:<br />fast-ready flood maps  pre-processing & RGB composition;<br />detailed flood maps segmentation approach.<br />Experimental results:<br />experiments on SAR images<br />Conclusions<br />“OPERA – Civil protection from floods” pilot project - Italian Space Agency & Italian Department for Civil Protection.<br />
  3. 3. Introduction<br />Multitemporal remote-sensing images represent a powerful source of information for monitoring the evolution of the Earth’s surface<br />Relevant task: identification of flooded areas.<br />SAR images are particularly useful during floods:<br />all-weather capability <br />cloud-penetrating properties<br />
  4. 4. Fast-ready flood maps<br />An RGB composition is used, where two images are combined into a false colour composite image  enhancing the flooded areas<br />Images can be acquired with different sensor parameters  an appropriate pre-processing is required<br />Three sequential steps are proposed: <br />filtering, <br />adaptive histogram truncation, <br />equalization.<br />
  5. 5. Filtering<br />Comparison of different filters: SRAD (Speckle Reducing Anisotropic Diffusion), Lee, Frost, Enhanced Lee and Frost filters.<br />SRAD allows to reduce noise and to preserve details.<br />Best performances in the frequency domain  mean preservation and isotropic behavior.<br />Original image Lee Frost<br /> Enhanced Lee Enhanced Frost SRAD<br />
  6. 6. Histogram Equalization<br />Linear shrinking from 2 Bytes to 1 Byte  loss of many informative contents, due to the very long distribution tail<br />Histogram equalization  normalization of the different histogram distributions<br />Usual histogram equalization is not properly working with such a heavy tail.<br /> Adaptive histogram truncation is applied<br />Zoom into the interval 0-500 of original histogram of image (maximum value = 18000)<br />Histogram of equalized image<br />
  7. 7. Histogram truncation & Equalization<br />Preliminary clipping to the 95th percentile & equalization  best performances<br />RGB composition image is obtained:<br />Red channel: difference between pre and post-event <br />Green: post-event image <br />Blue: pre-event image<br />Blue = uniformcumulative function Magenta = cumulative ofimage<br />Adaptivehistogramequalization (truncation& equalization)<br />Histogramequalizationoforiginalimage<br />
  8. 8. Detailed flood maps<br />A multi-seed-growing segmentation approach is employed.<br />Segmentation process:<br />uses filtered images; <br />starts from water pixels;<br />uses an anisotropic image-scanning mechanism  order of pixel analysis is dependent on the image content.<br />Test rule  a similarity criterion is satisfied.<br />
  9. 9. Segmentation algorithm <br />Given the seed point , a “seed region” is generated, using the seed pointand its direct 8-neighbours:<br />The sample mean is computed: <br />Sample standard deviation is computed on a 5x5 window <br /> centered on the seed pixel:<br />
  10. 10. Segmentation algorithm<br />Sample mean m aggregation rule<br />Sample standard deviation s estimate the threshold value. <br />The threshold is adaptive to the scattering of the region of interest and is set to:<br />A new pixel is assigned to the region if its distance with respect to the “seed region” is small enough. <br />
  11. 11. Data set<br />Different multitemporal data set consisting of pair of co-registered Cosmo/Skymed images are used. <br />Flood event of the MassaciuccoliLake: images in Stripmap acquisition modes, with different geometric acquisition parameters<br />Cosmo/SkymedStripmap images (spacial resolution: 2,5 meters)<br />LEFT: 20th December 2009 (ascending/right looking angle) <br />RIGHT: 30th December 2009 (descending/left looking angle)<br />
  12. 12. Example of Fast-ready flood map<br />The images could be used in an RGB composition despite the different acquisition parameters<br />RGB composition.<br />In magenta: change due to decrease of backscattering, corresponding to flooded areas.<br />In cyan: no-change due to high backscattering in both images<br />In bordeaux: no-change due to low backscattering in both images<br />
  13. 13. Example of Detailed flood map<br />The segmentation process is not affected by different acquisition setting  the filtered images can be used.<br />Detailed map of flooded areas.<br />In blue: steadywater<br />In cyan: flooded areas<br />
  14. 14. Otherexamples on Stripmapimages<br />Cosmo/Skymed images acquired near Scutari (Albania) in Stripmap mode (spatial resolution: 2,5 meters), with different acquisition parameters<br />10th January 2010 - in descending configuration with right look angle<br />15th January 2010 - in ascending configuration with right look angle<br />Fast-readyfloodmap<br />Detailedfloodmap<br />Floodedareas<br />Floodedareas<br />Steady water<br />Steady water<br />No floodedareas<br />Otherchanges<br />
  15. 15. Otherexamples on Stripmapimages<br />Cosmo/Skymed images acquired near Alessandria (Italy) in Stripmap mode (pixel resolution: 2,5 meters), in descending configuration withright look angle <br />30th April 2009<br />1st May 2009<br />Detailedfloodmap<br />Fast-readyfloodmap<br />Floodedareas<br />Floodedareas<br />No floodedareas<br />Steady water<br />Steady water<br />Otherchanges<br />
  16. 16. Examples on Spotlightimages<br />Cosmo/Skymed images acquired near Alessandria (Italy) in Spotlight mode (pixel resolution: 0,5 meters), with different acquisition parameters<br />1st May 2009 - in ascending configuration with right look angle<br />29th April 2009 - in descending configuration with left look angle<br />30th April 2009 - in ascending configuration with right look angle<br />Fast-readyfloodmap<br />Detailedfloodmap<br />Multitemporalfloodmap<br />Floodedareas at 29th April 2009<br />Floodedareas at 30th April 2009<br />Steady water<br />Floodedareas<br />No floodedareas<br />Floodedareas<br />Steady water<br />Steady water<br />Otherchanges<br />
  17. 17. Conclusions<br />Several image processing techniques and a segmentation method have been proposed.<br />Images acquired by the new mission Cosmo/Skymed have been used for experiments.<br />Both qualitative and quantitative algorithms have been presented and very good performances have been obtained in both cases. <br />

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