QUALITY ASSESSMENT OF DESPECKLED SAR IMAGES

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QUALITY ASSESSMENT OF DESPECKLED SAR IMAGES
Elena Angiati, Silvana Dellepiane
Department of Biophysical and Electronic
Engineering (DIBE)- Università di Genova- ITALY
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Outline

• Introduction
• despeckling filters and quality assessment of
  filtered images
• The proposed method
• statistical analysis
• novel frequency analysis.
• Experimental results
• Cosmo/Skymed images.
• Conclusions

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Introduction to speckle and quality assesment

• Speckle ? granular aspect of coherent imaging
  systems.
• Speckle reduction before image analysis steps
• feature detection,
• segmentation,
• classification.
• Different methods to assess the filtered image
  quantitatively.
• Results ? contradictory no reproduce the human
  perceptual interpretation.

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Methods for despekling

• First approach ? multi-looking processing
• ?linear moving-average filter
• blurs edges, decreases the image resolution, and
  cause a loss in image features.
• Different other approaches appeared in the
  literature ? Lee, Frost, Kuan and Gamma MAP
  filters.
• More recently the new method of
  Speckle-Reducing-Anisotropic-Diffusion (SRAD) has
  been proposed.

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Metrics for evaluation of despeckling filters

• The best filter has been selected when details
  and edges have been preserved
• Good filter ? the variance decreased without
  changing the mean.
• Some metrics require a speckle-free image
• ? important to find metrics not need free-noise
  image ? Speckle Suppression Index

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Proposed method Statistical analysis (1)

• Metrics criteria ? not require original image
  without noise (metrics presented in literature
  and two new indexes)
• M speckled image F filtered image

• New parameter Mean Preservation Index (MPI)
• only makes use of the sample mean, computed from
  a homogeneous region
• Speckle Suppression Index (SSI)
• The smaller the SSI value ? the greater the
  speckle suppression effect

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Proposed method Statistical analysis (2)

• Speckle Suppression and Mean Preservation Index
  (SSMPI)
• the mean difference between the speckled and
  filtered image is not normalized ? higher values
  for larger backscattering regions.
• New index Mean Preservation Speckle Suppression
  Index
• better comparison of various filters on different
  images.
• ? the lower values indicate better performance of
  the filter in terms of mean preservation and
  noise reduction.

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Proposed method Frequency analysis (1)

• Behavior of non linear filters ? desired
  properties of good image filters
• zero gain at zero frequency
• isotropic behavior.
• Non-linear filters are often subject to
  distortions and artifacts.
• No transfer function of a non-linear filter ?
  Equivalent Transfer Function

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Proposed method Frequency analysis (2)

• Mean-preservation ? Static Power Gain
• H2(0,0)ETF(0,0)
• Power gain that will be zero decibel for a
  perfectly preserving filter
• Static Power Gain is related to the MPI value
• Isotropy behavior ? 1D plots of Equivalent
  Transfer Function ETF(kx,0) and ETF(0,ky)
• The non monotonically behavior ? Stop-Band Ripple
  Amplitude

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Experimental results Dataset

• Dataset Cosmo/Skymed images
• 3 Spotlight (T1, T2, T3)
• 2 Stripmap (T4, T5)
• Filters used for comparison
• LEE
• FROST
• ENHANCED LEE
• ENHANCED FROST
• SRAD (two different parameters configuration)

(a)
Example Image acquired in Spotlight mode on
April 29th 2009. In red samples used for
statistical analysis and in green those used for
frequency analysis of (a) Water class and (b)
No-Water class.
(b)
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Qualitative analysis
Original image
(a) (b)
(c)
(d) (e)
(f)
Filtered images with different filters (a) Lee,
(b) Frost, (c) Enhanced Lee, (d) Enhanced Frost,
(e) SRAD with parameters 8-0,5, (f) SRAD with
parameters 200-0.01
(g) (h)
(i)
(l) (m)
(n)
Corresponding frequency domain of different
filters (g) Lee, (h) Frost, (i) Enhanced Lee,
(l) Enhanced Frost, (m) SRAD with parameters
8-0,5, (n) SRAD with parameters 200-0.01
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Experimental results Statistical analysis (1)
Mean Preservation Index for Spotlight images.
Water and No-Water class. Each value is
averaged over test samples
Speckle Suppression Index for Spotlight and
Stripmap images.
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Experimental results Statistical analysis (2)
Speckle Suppression and Mean Preservation Index
(SSMPI) for Spotlight images. Water class
Mean Preservation Speckle Suppression Index
(MPSSI) for Spotlight images. Water and
No-Water classes.
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Experimental results Frequency analysis (1)
Static Power Gain SRAD filter is the best in
mean preservation, as also proved by the MPI
index. This property is equally verified for
Water and No Water classes
Static Power Gain for Spotlight images. Water
and No-Water classes
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Experimental results Frequency analysis (2)
ETF analysis along different directions for
Spotlight images (T1, T2, T3)
(a)
(b)
(d)
(c)
(a) ETF(0,ky) for Water class (b) ETF(0,ky)
for No-Water class (c) ETF(kx,0) for Water
class (d) ETF(kx,0) for No-Water class.
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Experimental results Frequency analysis (3)
ETF analysis along different directions for
Stripmap images (T4, T5)
(b)
(a)
(d)
(c)
(a) ETF(0,ky) for Water class (b) ETF(0,ky)
for No-Water class (c) ETF(kx,0) for Water
class (d) ETF(kx,0) for No-Water class.
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Conclusions

• A method for the quality assessment of despeckled
  SAR images have been presented.
• Some new indexes are proposed, together with a
  new analysis in the frequency domain.
• Experiments on real data have been realized ?
  different acquisition mode different
  acquisition parameters.
• The proposed method is here used for the
  comparison of filters based on anisotropic
  diffusion, but it can be easily extended to other
  despeckling filters.

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Thank you for your attention!
Elena Angiati, Silvana Dellepiane
Department of Biophysical and Electronic
Engineering (DIBE)- Università di Genova- ITALY