Numerous hyperspectral algorithms have been developed to detect both full and subpixel solid target materials. Target signatures are obtained from spectral libraries that contain both target and nontarget materials. When the library is large and contains many potential targets, it is inefficient to run an individual detector for each material of interest. Additionally, such an approach produces numerous false alarms (i.e., multiple detections per pixel) due to spectral similarity among targets. In this article, we present an efficient approach for detecting multiple targets within large spectral libraries while mitigating false alarms. We first group spectrally similar materials within the library into a hierarchy of clusters. From each cluster containing a target material, a single detector is obtained. Each detector represents multiple library spectra, so an identification step is needed for detected pixels. Detected pixels are modeled as a mixture between their local in-scene background and candidate library spectra. Candidates are chosen from adjacent library clusters. The candidate model providing the best fit is chosen to report. Use of local background spectra provides a physically meaningful mixing model that adapts to detected pixels. Clustering the library reduces the computational complexity of modeling detected pixels. We demonstrate detection and false alarm mitigation performance of our proposed algorithm with a real hyperspectral dataset.

中文翻译：

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使用大型光谱库进行高效的高光谱目标检测和识别

已经开发了许多高光谱算法来检测全像素和亚像素固体目标材料。目标特征从包含目标和非目标材料的光谱库中获得。当库很大并且包含许多潜在目标时，为每种感兴趣的材料运行单独的检测器是低效的。此外，由于目标之间的光谱相似性，这种方法会产生大量错误警报（即，每个像素有多个检测）。在本文中，我们提出了一种有效的方法来检测大型光谱库中的多个目标，同时减少误报。我们首先将库中光谱相似的材料分组为一个聚类层次结构。从包含目标材料的每个簇中，获得单个检测器。每个检测器代表多个库光谱，因此需要对检测到的像素进行识别步骤。检测到的像素被建模为其局部场景背景和候选库光谱之间的混合。候选者是从相邻的图书馆群中选出的。选择提供最佳拟合的候选模型进行报告。局部背景光谱的使用提供了适应检测像素的物理上有意义的混合模型。对库进行聚类可降低对检测到的像素进行建模的计算复杂性。我们使用真实的高光谱数据集展示了我们提出的算法的检测和误报缓解性能。候选者是从相邻的图书馆群中选出的。选择提供最佳拟合的候选模型进行报告。局部背景光谱的使用提供了适应检测像素的物理上有意义的混合模型。对库进行聚类可降低对检测到的像素进行建模的计算复杂性。我们使用真实的高光谱数据集展示了我们提出的算法的检测和误报缓解性能。候选者是从相邻的图书馆群中选出的。选择提供最佳拟合的候选模型进行报告。局部背景光谱的使用提供了适应检测像素的物理上有意义的混合模型。对库进行聚类可降低对检测到的像素进行建模的计算复杂性。我们使用真实的高光谱数据集展示了我们提出的算法的检测和误报缓解性能。