Detection and tracking of gas plumes in LWIR hyperspectral video sequence data

Torin Gerhart; Justin Sunu; Lauren Lieu; Ekaterina Merkurjev; Jen-Mei Chang; Jérôme Gilles; Andrea L. Bertozzi

doi:10.1117/12.2015155

18 May 2013 Detection and tracking of gas plumes in LWIR hyperspectral video sequence data

Torin Gerhart, Justin Sunu, Lauren Lieu, Ekaterina Merkurjev, Jen-Mei Chang, Jérôme Gilles, Andrea L. Bertozzi

Proceedings Volume 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX; 87430J (2013) https://doi.org/10.1117/12.2015155
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Automated detection of chemical plumes presents a segmentation challenge. The segmentation problem for gas plumes is difficult due to the diffusive nature of the cloud. The advantage of considering hyperspectral images in the gas plume detection problem over the conventional RGB imagery is the presence of non-visual data, allowing for a richer representation of information. In this paper we present an effective method of visualizing hyperspectral video sequences containing chemical plumes and investigate the effectiveness of segmentation techniques on these post-processed videos. Our approach uses a combination of dimension reduction and histogram equalization to prepare the hyperspectral videos for segmentation. First, Principal Components Analysis (PCA) is used to reduce the dimension of the entire video sequence. This is done by projecting each pixel onto the first few Principal Components resulting in a type of spectral filter. Next, a Midway method for histogram equalization is used. These methods redistribute the intensity values in order to reduce icker between frames. This properly prepares these high-dimensional video sequences for more traditional segmentation techniques. We compare the ability of various clustering techniques to properly segment the chemical plume. These include K-means, spectral clustering, and the Ginzburg-Landau functional.

Citation Download Citation

Torin Gerhart, Justin Sunu, Lauren Lieu, Ekaterina Merkurjev, Jen-Mei Chang, Jérôme Gilles, and Andrea L. Bertozzi "Detection and tracking of gas plumes in LWIR hyperspectral video sequence data", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87430J (18 May 2013); https://doi.org/10.1117/12.2015155

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