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

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.

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

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.

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

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

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

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.

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:

aa yx ,

aaii yxiyxSG ,,1,

aa yxiii yxI

,

,9

1

aa yxW ,

aa yxWiii IyxI

,

22 ,24

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aa yxWiii yxII

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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

HT

HT

jj yxI ,

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)

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

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

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

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

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

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.