ieee gold angiati
TRANSCRIPT
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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
IEEE Gold Remote Sensing Conference 2010 Naval Academy, Livorno, Italy, April 29-30, 2010
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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.
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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
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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.
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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
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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
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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
Histogram equalization of original image Adaptive histogram equalization (truncation & equalization)
Blue = uniform cumulative function Magenta = cumulative of image
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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.
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Segmentation algorithm
• Given the seed point , a “seed region” is generated, using the seed point and its direct 8-neighbours:
• The sample mean is computed:
• Sample standard deviation is computed on a 5x5 window
centered on the seed pixel:
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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.
HT
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jj yxI ,
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Data set• Different multitemporal data set consisting of pair of co-registered
Cosmo/Skymed images are used. • Flood event of the Massaciuccoli Lake: images in Stripmap
acquisition modes, with different geometric acquisition parameters
Cosmo/Skymed Stripmap images (spacial resolution: 2,5 meters)LEFT: 20th December 2009 (ascending/right looking angle) RIGHT: 30th December 2009 (descending/left looking angle)
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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 imagesIn bordeaux: no-change due to low backscattering in both images
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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: steady waterIn cyan: flooded areas
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Other examples on Stripmap images 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
Fast-ready flood map Detailed flood map
Flooded areas
Steady water
No flooded areas
Other changes
Flooded areas
Steady water
15th January 2010 - in ascending configuration with right look angle
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Other examples on Stripmap images Cosmo/Skymed images acquired near Alessandria (Italy) in Stripmap mode (pixel resolution: 2,5 meters), in descending configuration with right look angle
30th April 2009 1st May 2009
Fast-ready flood map Detailed flood map
Flooded areas
Steady water
No flooded areas
Other changes
Flooded areas
Steady water
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Examples on Spotlight images Cosmo/Skymed images acquired near Alessandria (Italy) in Spotlight mode (pixel resolution: 0,5 meters), with different acquisition parameters
Fast-ready flood map Detailed flood map
Flooded areas
Steady water
No flooded areas
Other changes
Flooded areas
Steady water
29th April 2009 - in descending configuration with left look angle
30th April 2009 - in ascending configuration with right look angle
1st May 2009 - in ascending configuration with right look angle
Multitemporal flood map
Flooded areas at 29th April 2009
Steady water
Flooded areas at 30th April 2009
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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.