A signal-decomposed and interference-annihilated approach to hyperspectral target detection
A signal-decomposed and interference-annihilated approach to hyperspectral target detection
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DOI：
10.1109/tgrs.2003.821887
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发表时间：
2004-04
影响因子：
8.2
通讯作者：
Q. Du;Chein-I. Chang
中科院分区：
文献类型：
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作者：
Q. Du;Chein-I. Chang
高光谱成像传感器可以用非常窄的诊断波长揭示和发现目标。然而，它也可以提取许多未知的信号源，例如背景和自然签名以及不需要的人造物体，这些物体无法在视觉上或先验上识别。这些未知的信号源可以被称为干扰源，其通常在高光谱图像分析中比噪声起更主导的作用。从信号中分离出这种干扰物，然后在检测到之前将其消除，可能是一种更现实的方法。在许多应用中，感兴趣的信号可以进一步分为我们想要提取的期望信号和我们想要消除以增强信号检测能力的不期望信号。提出了一种信号分解与干扰消除（SDIA）方法在高光谱目标检测中的应用。它将干扰信号和不需要的信号视为单独的信号源，可以在目标检测之前消除。为此，本文提出了一种信号分解干扰/噪声（SDIN）模型。与建议SDIN模型，正交子空间投影为基础的模型和信号/背景/噪声模型可以包括作为其特殊情况。实验结果表明，基于SDIN模型的SDIA方法在目标检测和分类方面，总体上可以提高常用的广义似然比检验和约束能量最小化方法的性能。
A hyperspectral imaging sensor can reveal and uncover targets with very narrow diagnostic wavelengths. However, it comes at a price that it can also extract many unknown signal sources such as background and natural signatures as well as unwanted man-made objects, which cannot be identified visually or a priori. These unknown signal sources can be referred to as interferers, which generally play a more dominant role than noise in hyperspectral image analysis. Separating such interferers from signals and annihilating them subsequently prior to detection may be a more realistic approach. In many applications, the signals of interest can be further divided into desired signals for which we want to extract and undesired signals for which we want to eliminate to enhance signal detectability. This paper presents a signal-decomposed and interference-annihilated (SDIA) approach in applications of hyperspectral target detection. It treats interferers and undesired signals as separate signal sources that can be eliminated prior to target detection. In doing so, a signal-decomposed interference/noise (SDIN) model is suggested in this paper. With the proposed SDIN model, the orthogonal subspace projection-based model and the signal/background/noise model can be included as its special cases. As shown in the experiments, the SDIN model-based SDIA approach generally can improve the performance of the commonly used generalized-likelihood ratio test and constrained energy minimization approach on target detection and classification.