Publications/tochon.17.tgrs

Authors

Guillaume Tochon, Jocelyn Chanussot, Mauro Dalla Mura, Andrea Bertozzi

Journal

IEEE Transactions on Geoscience and Remote Sensing

Type

article

Projects

Olena

Keywords

Image

Date

2017-04-20

Abstract

It is now possible to collect hyperspectral video sequences at a near real-time frame rate. The wealth of spectral, spatial and temporal information of those sequences is appealing for various applications, but classical video processing techniques must be adapted to handle the high dimensionality and huge size of the data to process. In this article, we introduce a novel method based on the hierarchical analysis of hyperspectral video sequences to perform object tracking. This latter operation is tackled as a sequential object detection process, conducted on the hierarchical representation of the hyperspectral video frames. We apply the proposed methodology to the chemical gas plume tracking scenario and compare its performances with state-of-the-art methods, for two real hyperspectral video sequences, and show that the proposed approach performs at least equally well.

Bibtex (lrde.bib)

@Article{	  tochon.17.tgrs,
  author	= {Guillaume Tochon and Jocelyn Chanussot and Mauro Dalla
		  Mura and Andrea Bertozzi},
  title		= {Object tracking by hierarchical decomposition of
		  hyperspectral video sequences: application to chemical gas
		  plume tracking},
  journal	= {IEEE Transactions on Geoscience and Remote Sensing},
  year		= 2017,
  abstract	= {It is now possible to collect hyperspectral video
		  sequences at a near real-time frame rate. The wealth of
		  spectral, spatial and temporal information of those
		  sequences is appealing for various applications, but
		  classical video processing techniques must be adapted to
		  handle the high dimensionality and huge size of the data to
		  process. In this article, we introduce a novel method based
		  on the hierarchical analysis of hyperspectral video
		  sequences to perform object tracking. This latter operation
		  is tackled as a sequential object detection process,
		  conducted on the hierarchical representation of the
		  hyperspectral video frames. We apply the proposed
		  methodology to the chemical gas plume tracking scenario and
		  compare its performances with state-of-the-art methods, for
		  two real hyperspectral video sequences, and show that the
		  proposed approach performs at least equally well.},
  note		= {To appear.}
}