QUALITY ASSESSMENT OF
DESPECKLED SAR IMAGES
Elena Angiati, Silvana Dellepiane
Department of Biophysical and Electronic Engineering (DIBE)- Università di Genova- ITALY
Outline
• Introduction:
– despeckling filters and quality assessment of filtered
images;
• The proposed method:
– statistical analysis;
– novel frequency analysis.
• Experimental results:
– Cosmo/Skymed images.
• Conclusions
2
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.
3
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 SpeckleReducing-Anisotropic-Diffusion (SRAD) has been
proposed.
4
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
5
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
MPI 
 Mr   Fr
 Mr
• Speckle Suppression Index (SSI)
– The smaller the SSI value  the
greater the speckle suppression
effect
SSI 
sFr

 Mr
 Fr s Mr
6
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.
SSMPI  1   Mr
sFr
  Fr 
s Mr
• New index: Mean Preservation Speckle Suppression Index
– better comparison of various filters on different images.


MPSSI  1  Fr
  Mr
 sFr
sFr


MPI

 s
s Mr
 Mr
•  the lower values indicate better performance of the
filter in terms of mean preservation and noise reduction.
7
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”:
H (k x , k y )  ETF(k x , k y ) 
2
S F (k x , k y )
S M (k x , k y )
2
2
8
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
MPI  1  H (0,0)
• Isotropy behavior  1D plots of Equivalent
Transfer Function ETF(kx,0) and ETF(0,ky)
• The non monotonically behavior  Stop-Band
Ripple Amplitude
9
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)
10
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
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.
12
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.
13
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
14
Experimental results: Frequency analysis (2)
ETF analysis along different directions for Spotlight images (T1, T2, T3)
(a)
(b)
(c)
(d)
(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.
15
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.
16
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.
17
Thank you for your attention!
Elena Angiati, Silvana Dellepiane
Department of Biophysical and Electronic Engineering (DIBE)- Università di Genova- ITALY