How to Troubleshoot Apps for the Modern Connected Worker
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
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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
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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
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