Title
Sequential Tensor Decomposition For Gas Tracking In Lwir Hyperspectral Video Sequences
Abstract
With the development of hyperspectral imaging instruments, hyperspectral video sequences (HVS) can now be acquired with both high spectral and high temporal resolutions, allowing dynamic monitoring tasks such as gas tracking. However, the effective use of such large-scale sequential data also raises some challenges. Directly processing these data requires dramatic needs in terms of memory and computational loads. In this paper, we propose a novel method for gas tracking in HVS, based on decomposing sequential tensors into low-rank and error components, respectively. The gas target can be revealed from the error components corresponding to each frame. The global information contained in each frame and the correlation between adjacent frames are exploited by this tensor decomposition. Experiments are conducted on real HVS, assessing the good performances of the proposed method.
Year
DOI
Venue
2019
10.1109/WHISPERS.2019.8921385
2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Keywords
Field
DocType
Hyperspectral video sequences,tensor decomposition,chemical gas tracking
Sequential data,Pattern recognition,Tensor,Computer science,Matrix decomposition,Global information,Hyperspectral imaging,Stress (mechanics),Artificial intelligence,Sparse matrix,Tensor decomposition
Conference
ISSN
ISBN
Citations 
2158-6268
978-1-7281-5295-0
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Suling Tan100.34
Huan Liu2182.27
Yanfeng Gu374255.56
Jocelyn Chanussot44145272.11