SARFIRE X-SAR SEGMENTATION AND MOISTURE MODELING ROME, JULY 14 TH 2011
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1 SARFIRE: Spaceborn SAR imagery and environmental data fusion for the dinamic evaluation of land regions susceptibility to fire (CSK AO E-2288) SARFIRE X-SAR SEGMENTATION AND MOISTURE MODELING ROME, JULY 14 TH 2011 Silvia Canale, Alberto De Santis, Daniela Iacoviello, Fiora Pirri, Simone Sagratella Department of Computer and System Sciences Antonio Ruberti University of Rome Sapienza
2 OUTLINE X-SAR image segmentation Theory, model and results Moisture information from SAR images
3 X-SAR IMAGE SEGMENTATION The X-band is known to be sensitive to changes in the target moisture content, namely the response is lighter on increasing humidity. On the other hand different patterns on X- SAR images, corresponding to Regions with different characteristics, are distinguishable by the gray level spatial distribution (texture) only
4 X-SAR IMAGE SEGMENTATION A first step in the interpretation of SAR images is achieved by a segmentation procedure based on a discrete level set method, applied to a suitable transformation of the data, aiming at the enhancement of the texture properties that better describe the characteristics of the zones to be identified. The regions with different characteristics have different textures and therefore are distinguishable by some properties of the gray level spatial distribution
5 X-SAR IMAGE SEGMENTATION The most effective transformations we found so far are uniformity and local contrast. Uniformity is obtained as the sum of the square of the local image histogram bins value, evaluated on square neighbor of size 30 The local contrast is based on the local signal variance evaluated on square neighbor of size 30. Furthermore the contrast image is processed by a bank of rank filters obtained by varying kernel size and rank order.
6 X-SAR IMAGE SEGMENTATION Example of uniformity image: In order to identify the larger area of castel Fusano burned in the fire event of year 2000, the uniformity transformation is well suited, allowing the discrimination between the burned area and the neighboring regions
7 X-SAR IMAGE SEGMENTATION A four levels segmentation is obtained by successive image binarizations: first the image is partitioned into two distinct subregions (not necessarily simply connected) the obtained regions are binarized, obtaining a four levels segmentation
8 X-SAR IMAGE SEGMENTATION Identificazione zona Identificazione zona a bruciata filari
9 X-SAR IMAGE SEGMENTATION
10 MOISTURE INFORMATION The homogeneous zones identified by the segmentation can be monitored by a periodical acquisition of X-SAR images. Their average gray level value can be correlated with ground truth data obtained by the meteorological stations. The Aviation Digital Data Service ( provides the daily temperature and dew point values (24 hours) of Castel Fusano.
11 MOISTURE INFORMATION The dew point value represents the temperature to which a given parcel of air or, more precisely, water vapor, must be cooled down to condense into water at constant barometric pressure. It is strictly related to the humidity value RH. To retrieve this value we can consider a psychrometric chart or a look-up table, or by means of empirical relations such as the following:
12 MOISTURE INFORMATION where: E is the actual water vapor pressure (in units of millibar) Es is the saturated water vapor pressure (in units of millibar). These quantities can be evaluated using the dew point values (T d ) and the temperature values (T), both in Celsius scale: 7.5 T (237.7 T ) E E RH s E E s T d (237.7 T d )
13 MOISTURE INFORMATION The average relative humidity of the considered target area is obtained, and the values correlated with the identified subregions can be found by a simple proportion, using their average gray level value, evaluated over the segmented zones. Periodical image acquisition provides a time series RH moisture values for every subregion. The moisture content of each zone is strictly related with the weather conditions but also on the soil and vegetation characteristics.
14 MOISTURE INFORMATION Esempio: Dati Umidità- immagine Zona Livello di grigio Umidità Media= T 19 Td 15 E E s RH E E s 7.5T d /(237.7 T 7.5T /(237.7 T % d Zona Zona Zona Zona Zona Zona Zona Zona Zona9_ Zona Zona
15 MOISTURE INFORMATION
16 MOISTURE INFORMATION J C temp.aria Temperatura di rugiada Js in C con umidità relativa del: 30 % 35 % 40 % 45 % 50 % 55 % 60 % 65 % 70 % 75 % 80 % 85 % 30 C 10,5 C 12,9 C 14,9 C 16,8 C 18,4 C 20,0 C 21,4 C 22,7 C 23,9 C 25,1 C 26,2 C 27,2 C 29 C 9,7 C 12,0 C 14,0 C 15,9 C 17,5 C 19,0 C 20,4 C 21,7 C 23,0 C 24,1 C 25,2 C 26,2 C 28 C 8,8 C 11,1 C 13,1 C 15,0 C 16,6 C 18,1 C 19,5 C 20,8 C 22,0 C 23,2 C 24,2 C 25,2 C 27 C 8,0 C 10,2 C 12,2 C 14,1 C 15,7 C 17,2 C 18,6 C 19,9 C 21,1 C 22,2 C 23,3 C 24,3 C 26 C 7,1 C 9,4 C 11,4 C 13,2 C 14,8 C 16,3 C 17,6 C 18,9 C 20,1 C 21,2 C 22,3 C 23,3 C 25 C 6,2 C 8,5 C 10,5 C 12,2 C 13,9 C 15,3 C 16,7 C 18,0 C 19,1 C 20,3 C 21,1 C 22,3 C 24 C 5,4 C 7,6 C 9,8 C 11,3 C 12,9 C 14,4 C 15,8 C 17,0 C 18,2 C 19,3 C 20,3 C 21,3 C 23 C 4,5 C 6,7 C 8,7 C 10,4 C 12,0 C 13,5 C 14,8 C 16,1 C 17,2 C 18,3 C 19,4 C 20,3 C 22 C 3,6 C 5,9 C 7,8 C 9,5 C 11,1 C 12,5 C 13,9 C 15,1 C 16,3 C 17,4 C 18,4 C 19,4 C 21 C 2,8 C 5,0 C 6,9 C 8,6 C 10,2 C 11,6 C 12,9 C 14,2 C 15,3 C 16,4 C 17,4 C 18,4 C 20 C 1,9 C 4,1 C 6,0 C 7,7 C 9,3 C 10,7 C 12,0 C 13,2 C 14,4 C 15,4 C 16,4 C 17,4 C 19 C 1,0 C 3,2 C 5,1 C 6,8 C 8,3 C 9,8 C 11,1 C 12,3 C 13,4 C 14,5 C 15,5 C 16,4 C 18 C 0,2 C 2,3 C 4,2 C 5,9 C 7,4 C 8,8 C 10,1 C 11,3 C 12,5 C 13,5 C 14,5 C 15,4 C 17 C -0,6 C 1,4 C 3,3 C 5,0 C 6,5 C 7,9 C 9,2 C 10,4 C 11,5 C 12,5 C 13,3 C 14,5 C 16 C -1,4 C 0,5 C 2,4 C 4,1 C 5,6 C 7,0 C 8,2 C 9,4 C 10,5 C 11,6 C 12,6 C 13,5 C 15 C -2,2 C -0,3 C 1,5 C 3,2 C 4,7 C 6,1 C 7,3 C 8,5 C 9,6 C 10,6 C 11,6 C 12,5 C 14 C -2,9 C -1,0 C 0,6 C 2,3 C 3,7 C 5,1 C 6,4 C 7,5 C 8,6 C 9,6 C 10,6 C 11,5 C 13 C -3,7 C -1,9 C -0,1 C 1,3 C 2,8 C 4,2 C 5,5 C 6,6 C 7,7 C 8,7 C 9,6 C 10,5 C 12 C -4,5 C -2,6 C -1,0 C 0,4 C 1,9 C 3,2 C 4,5 C 5,7 C 6,7 C 7,7 C 8,7 C 9,6 C 11 C -5,2 C -3,4 C -1,8 C -0,4 C 1,0 C 2,3 C 3,5 C 4,7 C 5,8 C 6,7 C 7,7 C 8,6 C 10 C -6,0 C -4,2 C -2,6 C -1,2 C 0,1 C 1,4 C 2,6 C 3,7 C 4,8 C 5,8 C 6,7 C 7,6 C Esempio: alla temperatura interna di 20 C e alla temperatura superficiale di 9,3 C corrisponde un' umidità relativa del 50 %; ciò significa che, in queste condizioni, la condensa si formerà su tutte le superfici con temperatura superficiale uguale od inferiore a 9,3 C. In rapporto al valore di temperatura interna previsto e al valore di tempera superficiale ottenuto, si arriva a determinare, attraverso la Rome, tabella, July 14 il 2011 corrispondente punto d' incrocio, che permette di stabilire quanta umidità relativa può esserci nell' ambiente prima che inizi la formazione di condensa.
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