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

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 Nyström extension shows the ability to differentiate between different gasses while being able to group the similar items together, such as gasses or background signatures.