Matched affine joint subspace detection in remote hyperspectral reconnaissance

A. Schaum
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引用次数: 2

Abstract

The GLR (generalized likelihood ratio) test has been invoked for several decades as a prescription for generating target detection algorithms, when limited prior knowledge makes a theoretically ideal test inapplicable. Many popular HSI (hyperspectral imaging) detection algorithms rely ultimately on a GLR justification. However, experience with real-time remotely deployed detection systems indicates that certain heuristic modifications to the classic algorithm suite consistently produce better performance. A new target detection test, based on a Bayesian likelihood ratio (BLR) principle, has been used to explain these results and to define a broader class of detection algorithms. The more general approach facilitates the incorporation of prior beliefs, such as that gleaned from experience in measurement programs. A BLR test has been used to generate a new family of HSI algorithms, called matched affine joint subspace detection (MAJSD). Several examples from this class are described, and their utility is validated by detection comparisons.
远程高光谱侦察中的匹配仿射联合子空间检测
几十年来,当有限的先验知识使得理论上理想的测试不适用时,GLR(广义似然比)测试被用作生成目标检测算法的处方。许多流行的高光谱成像(HSI)检测算法最终依赖于GLR证明。然而,实时远程部署检测系统的经验表明,对经典算法套件的某些启发式修改始终会产生更好的性能。一种基于贝叶斯似然比(BLR)原理的新目标检测测试被用来解释这些结果,并定义了更广泛的检测算法类别。更普遍的方法促进了先前信念的结合,比如从测量程序的经验中收集的信念。一个BLR测试被用来生成一个新的HSI算法家族,称为匹配仿射关节子空间检测(MAJSD)。本文描述了该类中的几个示例,并通过检测比较验证了它们的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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