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Simultaneous spectral analysis of multiple video sequence data for LWIR gas plumes

机译：LWIR气体羽流的多个视频序列数据的同时频谱分析

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摘要

We consider the challenge of detection of chemical plumes in hyperspectral image data. Segmentation of gas is difficult due to the diffusive nature of the cloud. The use of hyperspectral imagery provides non-visual data for this problem, allowing for the utilization of a richer array of sensing information. We consider several videos of different gases taken with the same background scene. We investigate a technique known as "manifold denoising" to delineate different features in the hyperspectral frames. With manifold denoising, we can bring more pertinent eigenvectors to the forefront. One can also simultaneously analyze frames from multiple videos using efficient algorithms for high dimensional data such as spectral clustering combined with linear algebra methods that leverage either subsampling or sparsity in the data. Analysis of multiple frames by the Nystrom extension shows the ability to differentiate between different gasses while being able to group the similar items together, such as gasses or background signatures.
机译：我们考虑在高光谱图像数据中检测化学羽流的挑战。由于云的扩散性，很难进行气体分割。高光谱图像的使用为该问题提供了非可视数据，从而允许利用更丰富的传感信息阵列。我们考虑在同一背景场景下拍摄的几种不同气体的视频。我们研究了一种被称为“流形降噪”的技术来描绘高光谱帧中的不同特征。借助流形降噪，我们可以将更多相关的特征向量带到最前沿。人们还可以使用高效算法对高维数据同时分析来自多个视频的帧，例如将数据聚类与线性代数方法相结合的频谱聚类，这些方法利用了数据的二次采样或稀疏性。通过Nystrom扩展对多个帧进行的分析显示了能够区分不同气体的能力，同时能够将相似项目（例如气体或背景签名）组合在一起。

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