Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video
- Author(s): Merkurjev, E
- Sunu, J
- Bertozzi, AL
- et al.
Published Web Locationhttp://www.math.ucla.edu/~bertozzi/papers/ICIP-MBO-2014.pdf
© 2014 IEEE. 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. In this paper, we present a method to track and classify objects in hyperspectral videos. The method involves the application of a new algorithm recently developed for high dimensional data. It is made efficient by the application of spectral methods and the Nyström extension to calculate the eigenvalues/eigenvectors of the graph Laplacian. Results are shown on plume detection in LWIR standoff detection.
Many UC-authored scholarly publications are freely available on this site because of the UC Academic Senate's Open Access Policy. Let us know how this access is important for you.