公开(公告)号：US09189704B2

公开(公告)日：2015-11-17

申请号：US13870685

申请日：2013-04-25

Applicant: RAYTHEON COMPANY

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

IPC: G06K9/62 ,  G01N21/31 ,  G01J3/28 ,  G06K9/00 ,  G01N21/17 ,  G06K9/46

CPC classification number: G06K9/6202 ,  G01J3/28 ,  G01N21/31 ,  G01N2021/1793 ,  G06K9/00536 ,  G06K2009/00644 ,  G06K2009/4657 ,  G06T2207/10036

Abstract: Provided are examples of a detecting engine for determining in which pixels in a hyperspectral scene are materials of interest or targets present. A collection of spectral references, typically five to a few hundred, is used in look a through a million or more pixels per scene to identify detections. An example of the detecting engine identifies detections by calculating a kernel vector for each spectral reference in the collection. This calculation is quicker than the conventional Matched Filter kernel calculation which computes a kernel for each scene pixel. Another example of the detecting engine selects pixels with high detection filter scores and calculates coherence scores for these pixels. This calculation is more efficient than the conventional Adaptive Cosine/Coherence Estimator calculation that calculates a score for each scene pixel, most of which do not provide a detection.

Abstract translation: 提供了一种用于确定高光谱场景中的像素是存在感兴趣或目标的材料的检测引擎的示例。 频谱参考的集合，通常为5到几百个，用于每个场景看一百万个或更多个像素以识别检测。 检测引擎的示例通过计算集合中的每个频谱参考的核向量来识别检测。 该计算比传统的匹配滤波器内核计算更快，它计算每个场景像素的内核。 检测引擎的另一例子选择具有高检测滤波器分数的像素并且计算这些像素的相干分数。 该计算比传统的自适应余弦/相干估计器计算更有效，其计算每个场景像素的得分，其中大部分不提供检测。