GaoFen-3, the first polarimetric SAR satellite of China, carried out polarimetric calibration experiments using C-band polarimetric active radar calibrators (PARCs), trihedral corner reflectors (TCRs), and dihedral corner reflectors (DCRs). The calibration data were firstly processed referring to the classic 2 × 2 receive R and transmit T model for radar polarimeter systems, first proposed by Zebker, Zyl, and Held, and Freeman’s method based on PARCs, but the results were not good enough. After detailed analysis about the GaoFen-3 polarimetric system, we found that the system had some nonlinearity, then a new imbalance parameter was introduced to the classic model, which is equivalent to the γ proposed in Freeman’s paper about a general polarimetric system model. Then, we proposed the calibration data processing algorithm for GaoFen-3 based on the improved model and obtained better results. The algorithm proposed here is verified to be suitable for GaoFen-3 and can be applied to other spaceborne and airborne fully-polarimetric SAR systems.

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In a social network, the strength of relationships between users can significantly affect the stability of the network. In this paper, we use the k-truss model to measure the stability of a social network. To identify critical connections, we propose a novel problem, named k-truss minimization. Given a social network G and a budget b, it aims to find b edges for deletion which can lead to the maximum number of edge breaks in the k-truss of G. We show that the problem is NP-hard. To accelerate the computation, novel pruning rules are developed to reduce the candidate size. In addition, we propose an upper bound based strategy to further reduce the searching space. Comprehensive experiments are conducted over real social networks to demonstrate the efficiency and effectiveness of the proposed techniques.

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Abstract. This paper presents a novel and fast probabilistic method for white matter fiber tracking from diffusion weighted MRI (DWI). We formulate fiber tracking on a nonlinear state space model which is able to capture both smoothness regularity of fibers and uncertainties of the local fiber orientations due to noise and partial volume effects. The global tracking model is implemented using particle filtering, which allows us to recursively compute the posterior distribution of the potential fibers. The fiber orientation distribution is theoretically formulated for prolate and oblate tensors separately. Fast and efficient sampling is realised using the von Mises-Fisher distribution on unit spheres. Given a seed point, the method is able to rapidly locate the global optimal fiber and also provide a connectivity map. The proposed method is demonstrated on a brain dataset.