Model-based processing of multifrequency polarimetric SAR images of urban areas

In this paper, we describe a two-step classification scheme for fully polarimetric SAR images. The classification scheme is composed of the cascade of an optimum segmentation stage, and a NIL supervised classifier. Different segmentation schemes are described, specifically designed for mono- or multifrequency images. The classification scheme is applied to a set of fully polarimetric, multifrequency SIR-C images of the town of Pavia, in Northern Italy, considering all the possible pairs of polarimetric channels and the two bands individually and jointly, aiming at identifying the best combination for practical applications. Results show that for urban areas the best performance is achieved jointly processing the three polarimetric channels, and the minimum performance degradation is achieved considering the HH and the HV channels

Model-based segmentation techniques for multifrequency polarimetric SAR

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Optimum model-based segmentation techniques for multifrequency polarimetric SAR images of urban areas

A new technique, named diagonal polarimetric merge-using-moments (DPOL MUM), is proposed for the segmentation of multifrequency polarimetric synthetic aperture radar (SAR) images that exploits the characteristic block diagonal structure of their covariance matrix. This technique is based on the newly introduced split-merge test, which has a reduced fluctuation error than the straight extension of the polarimetric test (POL MUM) and is shown to yield a more accurate segmentation on simulated SAR images. DPOL MUM is especially useful in the extraction of information from urban areas that are characterized by the presence of different spectral and polarimetric characteristics. Its effectiveness is demonstrated by applying it to segment a set of SIR-C images of the town of Pavia. The classification of the image segmented with DPOL MUM shows higher probability of correct classification compared to POL MUM and to a similar technique that does not use the correlation properties (MT MUM)

A new maximum-likelihood joint segmentation technique for multitemporal SAR and multiband optical images

In this paper, we devise a new technique for the fusion of a sequence of multitemporal single-channel synthetic aperture radar (SAR) images of a given area with a single multiband optical image. Unlike for SAR, the availability of optical images is largely affected by atmospheric conditions, so that this is a case of practical interest. First, a statistical model for the joint distribution of SAR and optical data is provided. Then, a split-merge test based on this model is derived, and its performance is evaluated both analytically and using a Monte Carlo simulation. A new segmentation technique is introduced (OPT MUM), based on the test and on a region-growing scheme. The effectiveness of the proposed technique for the fusion of multitemporal SAR and multiband optical images is tested on synthetic and real images. Results show that the proposed scheme allows to both 1) discriminate characteristics that would be impossible to distinguish using only a single sensor and 2) increase the overall discrimination performance, even when each sensor has its own discrimination capability

CCDC 2020131: Experimental Crystal Structure Determination

Related Article: Pei Ma, Tiffany M. Smith Pellizzeri, Jon Zubieta, James T. Spencer|2020|J.Chem.Cryst.|50|14|doi:10.1007/s10870-018-0749-