Geng Xiurui, Sun Kang, Ji Luyan, Tang Hairong, Zhao Yongchao
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing, China.
Ministry of Education Key Laboratory for Earth System Modelling, Centre for Earth System Science, Tsinghua University, Beijing, China.
Sci Rep. 2015 Apr 15;5:9915. doi: 10.1038/srep09915.
Few band selection methods are specially designed for small target detection. It is well known that the information of small targets is most likely contained in non-Gaussian bands, where small targets are more easily separated from the background. On the other hand, correlation of band set also plays an important role in the small target detection. When the selected bands are highly correlated, it will be unbeneficial for the subsequent detection. However, the existing non-Gaussianity-based band selection methods have not taken the correlation of bands into account, which generally result in high correlation of obtained bands. In this paper, combining the third-order (third-order tensor) and second-order (correlation) statistics of bands, we define a new concept, named joint skewness, for multivariate data. Moreover, we also propose an easy-to-implement approach to estimate this index based on high-order singular value decomposition (HOSVD). Based on the definition of joint skewness, we present an unsupervised band selection for small target detection for hyperspectral data, named joint skewness band selection (JSBS). The evaluation results demonstrate that the bands selected by JSBS are very effective in terms of small target detection.
很少有波段选择方法是专门为小目标检测设计的。众所周知，小目标的信息很可能包含在非高斯波段中，在这些波段中小目标更容易与背景分离。另一方面，波段集的相关性在小目标检测中也起着重要作用。当所选波段高度相关时，这对后续检测是不利的。然而，现有的基于非高斯性的波段选择方法没有考虑波段的相关性，这通常导致所获得的波段具有高度相关性。在本文中，结合波段的三阶（三阶张量）和二阶（相关性）统计量，我们为多变量数据定义了一个新的概念，称为联合偏度。此外，我们还提出了一种基于高阶奇异值分解（HOSVD）的易于实现的方法来估计该指标。基于联合偏度的定义，我们提出了一种用于高光谱数据小目标检测的无监督波段选择方法，称为联合偏度波段选择（JSBS）。评估结果表明，JSBS选择的波段在小目标检测方面非常有效。
