Abstract:

The increasing popularity of hyperspectral sensors is dramatically increasing the temporal availability of data. To date, algorithms struggle to compare hyperspectral data collected across dates due to different environmental conditions during collection. In this work, we develop a temporal mapping in order to map data collected from one year to a different year. We investigated both conditional generative adversarial networks (cGANs) as well as affine transformations to perform this mapping. Both methods showed an improvement over using data from past collections without mapping, with cGANs outperforming the affine transformation.

Links:

Citation:

R. Fick, P. Gader, A. Zare, S. Meerdink, “Temporal Mapping of Hyperspectral Data,” 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp 1-4, Sept. 2019.

@Article{Fick2019TemporalMappingHSI,  
Title = {Temporal Mapping of Hyperspectral Data}, 
Author = {R. Fick and P. Gader and A. Zare and S. Meerdink}, 
Booktitle={10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},  
Year = {2019},  
pages={1-4},  
month={Sep.},
}