This paper presents a three-dimensional (3-D) target reconstruction characterized by its geometric components, i.e. canonical scattering objects. The target is decomposed into some primitive geometries, which are extracted to represent and characterize scattering features. The object parameters are estimated in the frequency domain based on the least square method, and all estimated objects uses the model-based reasoning rules to reconstruct 3-D target. The simulation of a simplified tank model is used to validate the feature extraction and target reconstruction.

To explore anisotropic information embedded among sub-aperture SAR images, we propose polarimetric sub-aperture singular value decomposition, where polarimetric and anisotropic features are simultaneously decomposed. The decomposed singular values and left singular vectors are equivalent to eigen-analysis-based polarimetric target decomposition, while the right singular vectors give the corresponding anisotropic feature vectors. A physics-based parameterization is proposed for anisotropic pattern, where two new parameters, namely, compactness and directivity, are proposed. Both simulation results and real SAR image analyses demonstrate that these new anisotropic parameters can identify specific types of scatterers.

In the booming era of high-resolution synthetic aperture radar (SAR) technology, SAR advanced information retrieval is critical for effective utilization of huge-volume SAR data. One important aspect of high-resolution SAR interpretation is to explore the anisotropic and dispersive information embedded among subaperture and subband SAR images. This paper formulates the polarimetric subaperture analysis as a singular-value decomposition problem, where polarimetric and anisotropic features can be simultaneously decomposed. The decomposed singular values and left singular vectors are equivalent to eigenanalysis-based polarimetric target decomposition, whereas the right singular vectors give the corresponding anisotropic feature vectors. A physics-based parameterization is proposed for anisotropic patterns, where two anisotropic entropy parameters, namely, compactness and directivity, are proposed. Both simulation results and real SAR image analyses demonstrate that these proposed anisotropic entropies can effectively identify specific types of scatterers depending on their geometric scale, curvature, and form of spatial distribution. The proposed anisotropic entropies could be applied to single- and dual-polarization high-resolution SAR data as well.

With the development of market economy in China, the problem of bad debt becomes increasingly serious in enterprises. In this paper, a bad-debt-risk evaluation model is established based on LS-SVM classifier, using a new set of index system which combines financial factors with non-financial factors on the basis of the 5C system evaluation method. The bad debt rating is separated into four classes- normality, attention, doubt and loss through analyzing accounts payable. Then the LS-SVM classifier is trained with 220 samples which are stochastically extracted from listed companies of China in industry, and the four classes are identified by the trained classifier using 80 samples. Then, BP neural network is also used to assess the same data. The experiment results show that LS-SVM has an excellent performance on training accuracy and reliability in credit risk assessment and achieves better performance than BP neural network.