公开(公告)号：US08995722B2

公开(公告)日：2015-03-31

申请号：US13959335

申请日：2013-08-05

Applicant: Raytheon Company

Inventor： Bradley A. Flanders ,  Ian S. Robinson ,  Anthony M. Sommese

IPC: G06K9/00 ,  G06K9/40 ,  G06K9/46

CPC classification number: G06K9/46 ,  G06F17/16 ,  G06K7/146 ,  G06K9/00557 ,  G06K9/0063 ,  G06K9/40 ,  G06K9/4609 ,  G06K9/6232 ,  G06K2009/00644 ,  G06K2009/4657 ,  G06T5/20 ,  G06T2207/10028

Abstract: The disclosure provides a filtering engine for selecting sparse filter components used to detect a material of interest (or specific target) in a hyperspectral imaging scene and applying the sparse filter to a plurality of pixels in the scene. The filtering engine transforms a spectral reference representing the material of interest to principal components space using the eigenvectors of the scene. It then ranks sparse filter components based on each transformed component of the spectral reference. The filtering engine selects sparse filter components based on their ranks. The filtering engine performs the subset selection quickly because the computations are minimized; it processes only the spectral reference vector and covariance matrix of the scene to do the subset selection rather than process a plurality of pixels in the scene, as is typically done. The spectral filter scores for the plurality of pixels are calculated efficiently using the sparse filter.

Abstract translation: 本公开提供了一种用于选择用于检测高光谱成像场景中的感兴趣材料（或特定目标）的稀疏滤波器组件并将该稀疏滤波器应用于场景中的多个像素的滤波引擎。 滤波引擎使用场景的特征向量将表示感兴趣的材料的频谱参考转换为主要分量空间。 然后根据频谱参考的每个变换分量对稀疏滤波器分量进行排序。 过滤引擎根据其排名选择稀疏过滤器组件。 过滤引擎快速执行子集选择，因为计算最小化; 如通常所做的那样，它仅处理场景的频谱参考矢量和协方差矩阵来进行子集选择，而不是处理场景中的多个像素。 使用稀疏滤波器有效地计算多个像素的频谱滤波器分数。

公开(公告)号：US20150036877A1

公开(公告)日：2015-02-05

申请号：US13959335

申请日：2013-08-05

Applicant: RAYTHEON COMPANY

Inventor： Bradley A. Flanders ,  Ian S. Robinson ,  Anthony M. Sommese

IPC: G06K9/46

CPC classification number: G06K9/46 ,  G06F17/16 ,  G06K7/146 ,  G06K9/00557 ,  G06K9/0063 ,  G06K9/40 ,  G06K9/4609 ,  G06K9/6232 ,  G06K2009/00644 ,  G06K2009/4657 ,  G06T5/20 ,  G06T2207/10028

Abstract: The disclosure provides a filtering engine for selecting sparse filter components used to detect a material of interest (or specific target) in a hyperspectral imaging scene and applying the sparse filter to a plurality of pixels in the scene. The filtering engine transforms a spectral reference representing the material of interest to principal components space using the eigenvectors of the scene. It then ranks sparse filter components based on each transformed component of the spectral reference. The filtering engine selects sparse filter components based on their ranks. The filtering engine performs the subset selection quickly because the computations are minimized; it processes only the spectral reference vector and covariance matrix of the scene to do the subset selection rather than process a plurality of pixels in the scene, as is typically done. The spectral filter scores for the plurality of pixels are calculated efficiently using the sparse filter.

Abstract translation: 本公开提供了一种用于选择用于检测高光谱成像场景中的感兴趣材料（或特定目标）的稀疏滤波器组件并将该稀疏滤波器应用于场景中的多个像素的滤波引擎。 滤波引擎使用场景的特征向量将表示感兴趣的材料的频谱参考转换为主要分量空间。 然后根据频谱参考的每个变换分量对稀疏滤波器分量进行排序。 过滤引擎根据其排名选择稀疏过滤器组件。 过滤引擎快速执行子集选择，因为计算最小化; 如通常所做的那样，它仅处理场景的频谱参考矢量和协方差矩阵来进行子集选择，而不是处理场景中的多个像素。 使用稀疏滤波器有效地计算多个像素的频谱滤波器分数。