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Automatic features extraction in  sub-urban landscape using very high resolution Cosmo-SkyMed SAR images Fabio Del Frate, Chiara  Pratola, Giovanni Schiavon, Domenico Solimini IGARSS 2011 – International Geoscience And Remote Sensing Symposium  Tor Vergata University, Rome - Italy Earth Observation Laboratory
COstellation of Small Satellites for the Mediterranean basin Observation  ,[object Object],[object Object],COSMO-SKYMED MISSION ,[object Object],IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  ,[object Object]
TEST SITE Tor Vergata area in Rome, Italy World View 2 image, 10 th  February 2010 IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  Cosmo-SkyMed image, 9 th  July 2010 Central area of the University Campus  Residential area Business building Shopping mall Buildings
IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  Streets Motorway Medium-size street Narrow street TEST SITE Tor Vergata area in Rome, Italy World View 2 image, 10 th  February 2010 Cosmo-SkyMed image, 9 th  July 2010
IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  Natural areas Grassland Cultivated Field Trees Bare soil TEST SITE Tor Vergata area in Rome, Italy World View 2 image, 10 th  February 2010 Cosmo-SkyMed image, 9 th  July 2010
CLASSIFICATION ALGORITHM ,[object Object],IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  NNs are recognized as rather competitive algorithms but one has to be very careful to avoid overfitting in the training phase
HIDDEN LAYERS CLASSIFICATION ALGORITHM WHAT IN INPUT ? WHAT IN OUTPUT ? ,[object Object],[object Object],[object Object],Asphalt Short vegetation  + bare soil Tall vegetation + trees Manmade
GLCM Gray Level Co-Occurrence Matrix (Haralick et al., 1973) IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  The matrix is computed with reference to a predefined box in the image, to a predefined number of gray levels, pixel distance ( d ) and direction ( q ) 4 gray levels (0, 1, 2, 3) d=1 and q =0° The element  P ij  of the matrix says how many times the element with gray level  i  is distant  d  pixels, in  q  direction, from an element with gray level  j 0  1  2  3 0 1 2 3 0 0 1 1 0 0 1 1 0 2 2 2 2 2 3 3
GLCM TEXTURE INFORMATION GLCM parameters ,[object Object],[object Object],[object Object],Investigation on the most suitable parameters FIXED ,[object Object],d = 15 q  = 45° 64 GLCM measures SEARCH FOR OPTIMUM VALUE/MEASURE Transformed Divergence (TD) (Bartolucci et al, 1983) ,[object Object],Max[ TD(i, j )] = 2  If  TD(i, j ) ≥ 1.9  then classes  i  and  j  are well distinguishable
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],TEST IMAGE IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
BUILDINGS EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
BUILDINGS EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
ASPHALT EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
ASPHALT EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
VEGETATION EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
VEGETATION EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  GLCM texture information Analysis Variation of the TD measure with the window size
IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  Mean values of the GLCM measures computed on the 4 classes GLCM texture information Analysis
IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  ,[object Object],[object Object],[object Object],[object Object],Overall accuracy: 81.8% NN TOPOLOGY: 4x12x12x4 CLASSIFICATION RESULTS INPUTS Class Training Validation A 3557 1336 LV 3347 1316 T 3274 1365 MM 3338 1354 Tot 13516 5371
IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],NN: 4x12x12x4 (81.8%) NN: 3x12x12x4 (73.5%)  CLASSIFICATION RESULTS
IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  Asphalt Low vegetation Trees Manmade structures 3 INPUT (UP) 4 INPUT (DOWN) CLASSIFICATION RESULTS
[object Object],[object Object],[object Object],[object Object],[object Object],Fully Automatic Classification IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  8 th  June 2010 9 th  July 2010 10 th  July 2010
8th June 2010 9th July 2010 10th July 2010 TRAINING SET (4200 pixels for each image) and VALIDATION SET (1800 pixels for each image) TRAINED NEURAL NETWORK (3x9x9x3) IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  Overall accuracy: 80.9% Fully Automatic Classification Asphalt Natural areas Manmade structures
CONCLUSIONS ,[object Object],Ongoing:  ,[object Object],IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  ,[object Object],[object Object]
ACKNOWLEDGEMENTS Cosmo-SkyMed images provided by ASI - AO project 1484, agreement N. I/061/09/0 FUTURE DEVELOPMENTS  ,[object Object],IGARSS 2011 – 24-29 July 2011 Vancouver, Canada  Old CSK Image New CSK Image Features Stack 1 Features Stack 2 Multi Temporal Operator NN1 Classification  MAP 1 NN2 Classification  MAP 2 NN3  Classification  CHANGE MASK CHANGE MAP MAP 1 -MAP 2 AND NAHIRI Change Detection

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presentazione_IGARSS2011.ppt

  • 1. Automatic features extraction in sub-urban landscape using very high resolution Cosmo-SkyMed SAR images Fabio Del Frate, Chiara Pratola, Giovanni Schiavon, Domenico Solimini IGARSS 2011 – International Geoscience And Remote Sensing Symposium Tor Vergata University, Rome - Italy Earth Observation Laboratory
  • 2.
  • 3. TEST SITE Tor Vergata area in Rome, Italy World View 2 image, 10 th February 2010 IGARSS 2011 – 24-29 July 2011 Vancouver, Canada Cosmo-SkyMed image, 9 th July 2010 Central area of the University Campus Residential area Business building Shopping mall Buildings
  • 4. IGARSS 2011 – 24-29 July 2011 Vancouver, Canada Streets Motorway Medium-size street Narrow street TEST SITE Tor Vergata area in Rome, Italy World View 2 image, 10 th February 2010 Cosmo-SkyMed image, 9 th July 2010
  • 5. IGARSS 2011 – 24-29 July 2011 Vancouver, Canada Natural areas Grassland Cultivated Field Trees Bare soil TEST SITE Tor Vergata area in Rome, Italy World View 2 image, 10 th February 2010 Cosmo-SkyMed image, 9 th July 2010
  • 6.
  • 7.
  • 8. GLCM Gray Level Co-Occurrence Matrix (Haralick et al., 1973) IGARSS 2011 – 24-29 July 2011 Vancouver, Canada The matrix is computed with reference to a predefined box in the image, to a predefined number of gray levels, pixel distance ( d ) and direction ( q ) 4 gray levels (0, 1, 2, 3) d=1 and q =0° The element P ij of the matrix says how many times the element with gray level i is distant d pixels, in q direction, from an element with gray level j 0 1 2 3 0 1 2 3 0 0 1 1 0 0 1 1 0 2 2 2 2 2 3 3
  • 9.
  • 10.
  • 11. BUILDINGS EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
  • 12. BUILDINGS EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
  • 13. ASPHALT EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
  • 14. ASPHALT EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
  • 15. VEGETATION EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
  • 16. VEGETATION EXAMPLES IGARSS 2011 – 24-29 July 2011 Vancouver, Canada
  • 17. IGARSS 2011 – 24-29 July 2011 Vancouver, Canada GLCM texture information Analysis Variation of the TD measure with the window size
  • 18. IGARSS 2011 – 24-29 July 2011 Vancouver, Canada Mean values of the GLCM measures computed on the 4 classes GLCM texture information Analysis
  • 19.
  • 20.
  • 21. IGARSS 2011 – 24-29 July 2011 Vancouver, Canada Asphalt Low vegetation Trees Manmade structures 3 INPUT (UP) 4 INPUT (DOWN) CLASSIFICATION RESULTS
  • 22.
  • 23. 8th June 2010 9th July 2010 10th July 2010 TRAINING SET (4200 pixels for each image) and VALIDATION SET (1800 pixels for each image) TRAINED NEURAL NETWORK (3x9x9x3) IGARSS 2011 – 24-29 July 2011 Vancouver, Canada Overall accuracy: 80.9% Fully Automatic Classification Asphalt Natural areas Manmade structures
  • 24.
  • 25.