Automated Target Detection and Discrimination Using Constrained Kurtosis Maximization

Exploiting hyperspectral imagery without prior information is a challenge. Under this circumstance, unsupervised target detection becomes an anomaly detection problem. We propose an effective algorithm for target detection and discrimination based on the normalized fourth central moment named kurtosis, which can measure the flatness of a distribution. Small targets in hyperspectral imagery contribute to the tail of a distribution, thus making it heavier. The Gaussian distribution is completely determined by the first two order statistics and has zero kurtosis. Consequently, kurtosis measures the deviation of a distribution from the background and is suitable for anomaly/target detection. When imposing appropriate inequality constraints on the kurtosis to be maximized, the resulting constrained kurtosis maximization (CKM) algorithm will be able to quickly detect small targets with several projections. Compared to the widely used unconstrained kurtosis maximization algorithm, i.e., fast independent component analysis, the CKM algorithm may detect small targets with fewer projections and yield a slightly higher detection rate.     

A Microwave Tomographic Approach for Nondestructive Testing of Dielectric Coated Metallic Surfaces

A microwave imaging method for nondestructive testing of perfectly conducting surfaces beyond a layered media is presented. The method is an adaptation of the surface reconstruction approach by Yapar et al. to the present problem. It is based on the analytical continuation of the measured data to the surface under test through a special representation of the scattered field in terms of Fourier transform and Taylor expansion. Then the problem is reduced to the solution of a nonlinear equation which is solved iteratively via the Newton method and regularization in the least squares sense. Numerical simulations show that defects as small as lambda/500 can be recovered through the presented algorithm.     

贝加尔裂谷系及邻区断裂现今活动变化及成因(英文)

已有的地质和地球物理方法无法对年、月、旬等现今时间尺度上的断层活动变化进行有效预测。运用基于地震活动定量指数运算程序的地理信息技术可以解决这个问题。这种方法被应用于贝加尔裂谷系(BRS)及邻区的研究当中。研究发现,断层活动变化发生在以几年为周期的时间尺度上,这无法用区域应力场的变化进行合理解释。沿着穿越贝加尔裂谷系的剖面编制了活动断层图以及地震活动定量指数曲线。提出的这种方法可以根据地震活动定量指数对活动断层进行仔细的分类,从而为解决地震中期预报相关问题开辟了重要途径。这种方法还被用来研究断层现今活动的时空变化和形成机制。研究发现,在实时尺度上断层活动具有高频发生的特征,这种现象可能是由于脆性岩石圈板内和块体内部运动产生的慢变形波的干扰导致的。利用变形波的通过速度可以对活动断层进行分组,各组具有不同的地质和地球物理特征参数,同时还可以对变形波波前的方向以及某一区域内主要断层在实时间隔内(地质上的瞬间)的活动情况进行预测。