A few studies have shown that polarimetric SAR data sets may be
useful for discriminating different urban environments. In particular, in 
the AIRSAR data over Sydney have been considered, and the potentials for
characterizing different building clusters by extracting single, double and
triple bounce effects have been demonstrated. Similarly, in  the different
statistical properties of built aggregates in a urban area using SIR-C
measurements have been discussed in order to provide a basis for an efficient
segmentation of polarimetric urban SAR data sets. Finally, the availability of
SRL-1, 2 and possibly SRTM mission allows considering multitemporal and
multiparametric characterization of urban areas discussing the mutual effects
of temporal/polarimetric redundancy . To provide a quantitative discussion
on the usefulness and the limits of SAR data sets at the spatial resolution
that satellite sensor are currently able to provide, and to further validate
these results, a comprehensive study is proposed in this paper using ERS,
RADARSAT and SIR-C/X-SAR data over the town of Pavia, northern Italy.
Using nine ERS images, 5 SIR-C/X-SAR data sets and one RADARSAT
image over the same urban area, many investigations may be produced. The
precise knowledge of the site and the availability of a well tested neural
classifier allows a quantitative comparison on both the possibility to
discriminate among different urban environments (city center vs. residential
areas vs. sparse buildings) and among some of the most important land
cover/land use classes (built up areas, streets and railways, vegetation,
water...). In particular, the neural kernel-based classifier is based on a
two-step approach, performing first the image segmentation on a pixel-by-pixel
basis. Then, the spatial analysis is carried out by means of a second
classification, but using as input vector the percentages of pixels in a window
around the current pixel position assigned to each class by the first
classifier. These steps are implemented either by an unsupervised neural
network (ART-2), followed by a fuzzy clustering using a standard Fuzzy-C-Means
(FCM) algorithm , or a supervised fuzzy ARTMAP structure . Results and
conclusions We compare classification results obtained by using polarimetric,
multitemporal or multitemporal/polarimetric data sets, aggregating SAR
polarizations or dates and exploring also the interactions among SAR data
recorded by different sensors. Since SIR-C/X-SAR data were recorded in April
1994 , they partially overlap with the ERS images, providing a unique set for
this kind of analysis. Classifications are obtained also by exploiting texture
measures for a better discrimination of urban environments. We want to discuss
which are the advantages of a polarimetric data sets, and to evaluate the
usefulness, for our purposes, of using more polarizations with respect to a
combination of multitemporal single polarization images. This could also be
used to determine which of the many functioning modes of the ASAR sensor would
be most useful for urban mapping and/or monitoring applications.
 Y. Dong, B. Forster, and C. Ticehurst, 'A new decomposition of radar
polarization signatures,' IEEE Trans. Geosci. Remote Sensing, vol. 36, pp.
933-939, May 1998.
 E. Costamagna, P. Gamba, P. Lombardo, G. Chinino:
"Statistical analysis and neuro-fuzzy classification of polarimetric SAR images
of urban areas", Proc. of the ERS/ENVISAT Symposium, Gotheborg, Sweden, Oct. 2000.
 F. Dell"Acqua, P. Gamba, P. Lombardo, T. Macrì Pellizzeri, D. Mazzola:
"Multiband SAR classification using contextual analysis: annealing segmentation
vs. a neural kernel-based approach", Proc. of IGARSS"02, Toronto (CAN), June
2002, Vol. V, pp. 2593-2595.
 P. Gamba, B. Houshmand: "An efficient neural
classification chain for optical and SAR
urban images", International Journal
of Remote Sensing, Vol. 22, n. 8, pp. 1535-1553, May 2001.  G. Amici, F.
Dell"Acqua, P. Gamba, G. Pulina: "Fuzzy, neural and neuro-fuzzy classification
of pre- and post-event SAR images for flood monitoring and disaster
mitigation", Proc. of the First International Workshop on Multi-temporal
Analysis of Remote Sensing Images, Trento, Italy, 13-14 Sept. 2001.