This paper addresses the problem of estimating the azimuth antenna pattern by using a set of persistent point scatterers (PPS) retrieved from a stack of interferometric synthetic aperture radar images. This is achieved by means of a maximum likelihood estimation. PPS emerge as a restricted subset of the well known persistent scatterers, for which many applications have been described in the literature. PPS have a more stringent property since they explicitly require an impulsive trend feature; a good degree of isolation from the neighboring targets is further necessary to estimate the antenna pattern by means of digital spotlight focusing. A statistical model for PPS is provided and experimentally validated; the sufficient number of PPSs necessary to get a given accuracy for the azimuth antenna estimation is also suggested. Results using both simulated and real X-band Cosmo Skymed data are eventually illustrated.

A correct recognition of nonverbal expressions is currently one of the most important challenges of research in the field of human computer interaction. The ability to recognize human actions could change the way to interact with machines, maybe the way to live. In this paper, the innovative recognition system developed in the Italian research project PON SS-RR, finalized to support the classification process of the two behavioral situations (resonance and dissonance) of a candidate applying for a job position, is focused and described.

A space adaptive flexible block quantizer (SA-FBQ), suited for spaceborne synthetic aperture radar missions, is presented. This quantizer is actually an extension of the flexible dynamic block adaptive quantizer (FDBAQ), proposed in [1], which, in turn, is an extension of the block adaptive quantizer (BAQ). The BAQ is the optimal quantizer for a homogeneous target. The FDBAQ gets better performances on heterogeneous targets by adaptively selecting the best BAQ according to the local signal-to-thermal-noise ratio: The worst the SNTR, the lower the quantizer rate. The quantizer selection is precomputed in a lookup table (LUT), by assuming a fixed and known probability distribution function (pdf) of the reflectivity σ0. The SA-FBQ extends further this concept allowing the reflectivity pdf to vary, coping with this by exploiting many LUTs (i.e., quantizer set), each adapted to the local statistics. In this paper, we introduce an algorithm to adaptively find the best set of quantizers constrained on the mean bit rate; we discuss the implementation of the SA-FBQ, and we estimate its performances in comparison with the FDBAQ and the BAQ under different scenarios. Preliminary results are shown by exploiting the worldwide mosaic of C-band reflectivity derived from European Space Agency ENVISAT data and Sentinel-1 system parameters.