IGARSS2011_Angiati_Dellepiane.ppt

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  • Good morning, my name is Elena Angiati and this paper is about identification of roofs perimeter from aerial and satellite images
  • IGARSS2011_Angiati_Dellepiane.ppt

    1. 1. QUALITY ASSESSMENT OF DESPECKLED SAR IMAGES Department of Biophysical and Electronic Engineering (DIBE)- Università di Genova- ITALY Elena Angiati, Silvana Dellepiane
    2. 2. Outline <ul><li>Introduction: </li></ul><ul><ul><li>despeckling filters and quality assessment of filtered images; </li></ul></ul><ul><li>The proposed method: </li></ul><ul><ul><li>statistical analysis; </li></ul></ul><ul><ul><li>novel frequency analysis. </li></ul></ul><ul><li>Experimental results: </li></ul><ul><ul><li>Cosmo/Skymed images. </li></ul></ul><ul><li>Conclusions </li></ul>
    3. 3. Introduction to speckle and quality assesment <ul><li>Speckle  granular aspect of coherent imaging systems. </li></ul><ul><li>Speckle reduction before image analysis steps: </li></ul><ul><ul><li>feature detection, </li></ul></ul><ul><ul><li>segmentation, </li></ul></ul><ul><ul><li>classification. </li></ul></ul><ul><li>Different methods to assess the filtered image quantitatively. </li></ul><ul><li>Results  contradictory & no reproduce the human perceptual interpretation. </li></ul>
    4. 4. Methods for despekling <ul><li>First approach  “multi-looking processing”: </li></ul><ul><ul><li> linear moving-average filter </li></ul></ul><ul><ul><li>blurs edges, decreases the image resolution, and cause a loss in image features. </li></ul></ul><ul><li>Different other approaches appeared in the literature  Lee, Frost, Kuan and Gamma MAP filters. </li></ul><ul><li>More recently the new method of Speckle-Reducing-Anisotropic-Diffusion (SRAD) has been proposed. </li></ul>
    5. 5. Metrics for evaluation of despeckling filters <ul><li>The best filter has been selected when details and edges have been preserved </li></ul><ul><li>Good filter  the variance decreased without changing the mean. </li></ul><ul><li>Some metrics require a speckle-free image : </li></ul><ul><li> important to find metrics not need free-noise image  Speckle Suppression Index </li></ul>
    6. 6. Proposed method: Statistical analysis (1) <ul><li>Metrics & criteria  not require original image without noise (metrics presented in literature and two new indexes) </li></ul><ul><ul><li>M = speckled image; F = filtered image </li></ul></ul><ul><li>New parameter: Mean Preservation Index (MPI) </li></ul><ul><ul><li>only makes use of the sample mean, computed from a homogeneous region </li></ul></ul><ul><li>Speckle Suppression Index (SSI) </li></ul><ul><ul><li>The smaller the SSI value  the greater the speckle suppression effect </li></ul></ul>
    7. 7. Proposed method: Statistical analysis (2) <ul><li>Speckle Suppression and Mean Preservation Index (SSMPI): </li></ul><ul><ul><li>the mean difference between the speckled and filtered image is not normalized  higher values for larger backscattering regions. </li></ul></ul><ul><li>New index: Mean Preservation Speckle Suppression Index </li></ul><ul><ul><li>better comparison of various filters on different images. </li></ul></ul><ul><li> the lower values indicate better performance of the filter in terms of mean preservation and noise reduction. </li></ul>
    8. 8. Proposed method: Frequency analysis (1) <ul><li>Behavior of non linear filters  desired properties of good image filters: </li></ul><ul><ul><li>zero gain at zero frequency </li></ul></ul><ul><ul><li>isotropic behavior. </li></ul></ul><ul><li>Non-linear filters are often subject to distortions and artifacts. </li></ul><ul><li>No transfer function of a non-linear filter  “Equivalent Transfer Function”: </li></ul>
    9. 9. Proposed method: Frequency analysis (2) <ul><li>Mean-preservation  “Static Power Gain” </li></ul><ul><li>H 2 (0,0)=ETF(0,0) </li></ul><ul><ul><li>Power gain that will be zero decibel for a perfectly preserving filter </li></ul></ul><ul><ul><li>Static Power Gain is related to the MPI value </li></ul></ul><ul><li>Isotropy behavior  1D plots of Equivalent Transfer Function ETF(k x ,0) and ETF(0,k y ) </li></ul><ul><li>The non monotonically behavior  Stop-Band Ripple Amplitude </li></ul>
    10. 10. Experimental results: Dataset Example : Image acquired in Spotlight mode on April 29 th 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. <ul><li>Dataset: Cosmo/Skymed images: </li></ul><ul><li>3 Spotlight (T1, T2, T3) </li></ul><ul><li>2 Stripmap (T4, T5) </li></ul><ul><li>Filters used for comparison : </li></ul><ul><li>LEE </li></ul><ul><li>FROST </li></ul><ul><li>ENHANCED LEE </li></ul><ul><li>ENHANCED FROST </li></ul><ul><li>SRAD (two different parameters configuration) </li></ul>(a) (b)
    11. 11. Qualitative analysis 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 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 (a) (b) (c) (d) (e) (f) (g) (h) (i) (l) (m) (n) Original image
    12. 12. 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.
    13. 13. Experimental results: Statistical analysis (2) Mean Preservation Speckle Suppression Index (MPSSI) for Spotlight images. “Water” and “No-Water” classes. Speckle Suppression and Mean Preservation Index (SSMPI) for Spotlight images. “Water” class
    14. 14. 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
    15. 15. Experimental results: Frequency analysis (2) (a) ETF(0,k y ) for “Water” class; (b) ETF(0,k y ) for “No-Water” class; (c) ETF(k x ,0) for “Water” class; (d) ETF(k x ,0) for “No-Water” class. ETF analysis along different directions for Spotlight images (T1, T2, T3) (a) (b) (d) (c)
    16. 16. Experimental results: Frequency analysis (3) (a) ETF(0,k y ) for “Water” class; (b) ETF(0,k y ) for “No-Water” class; (c) ETF(k x ,0) for “Water” class; (d) ETF(k x ,0) for “No-Water” class. ETF analysis along different directions for Stripmap images (T4, T5) (a) (b) (d) (c)
    17. 17. Conclusions <ul><li>A method for the quality assessment of despeckled SAR images have been presented. </li></ul><ul><li>Some new indexes are proposed, together with a new analysis in the frequency domain. </li></ul><ul><li>Experiments on real data have been realized  different acquisition mode & different acquisition parameters. </li></ul><ul><li>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. </li></ul>
    18. 18. Thank you for your attention! Department of Biophysical and Electronic Engineering (DIBE)- Università di Genova- ITALY Elena Angiati, Silvana Dellepiane

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