PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
October 2016
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Pan images and a better SNR than the high resolution MS
images directly collected by a high resolution MS sensor. This
in turn confirmed that the simultaneous collection of a high
resolution Pan image and a low resolution MS image is an
effective solution to overcome the physical limitations of the
sensor systems to achieve high resolution MS images with
high SNR.
In addition, the combination of a high resolution Pan image
and a low resolution MS image can significantly reduce the
data volume of satellite imagery. This extends the on-board
storage capacity and speeds up the data downlink process
from satellite to ground.
SNR, spatial resolution, and data volume are challeng-
ing problems in hyperspectral remote sensing. The results
demonstrated in this study—acquiring a low resolution MS
to increase the SNR and reduce the data volume, and success-
fully using pansharpening to increase the spatial resolution
of MS images—unveils the potential of using pansharpening
to increase the SNR and spatial resolution of hyperspectral
images, and reduce their data volumes. Because of the signif-
icant bandwidth difference between a Pan image and a hy-
perspectral image, it may be possible to use a high resolution
MS band to “ms”-sharpen the corresponding low resolution
hyperspectral bands, and then use a higher resolution Pan
band to pan-sharpen the corresponding “ms”-sharpened hy-
perspectral bands. Research on this topic is being conducted
in our lab.
A
cknowledgments
This research was sponsored by the Atlantic Innovation Fund
(AIF) of the Atlantic Canada Opportunities Agency (ACOA).
The authors sincerely thank Dr. Paul Hink, President of i2
innovation and former VP Business Development & Strat-
egy of PHOTONIS USA, Inc., and Dr. Donald Prévost and
his Vision Program team at the National Optics Institute of
Canada (INO) for providing tangible guidance and support
on the photo lab setting, image collection, and quality mea-
surements. The first author also greatly appreciates the op-
portunity to work in Prof. Ramesh Raskar’s Camera Culture
research lab at the Massachusetts Institute of Technology
(MIT) and the scientific discussions with his team on image
quality and new sensor technologies.
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A
bout
the
A
uthors
Yun Zhang is a professor at the Department of Geodesy and
Geomatics Engineering of the University of New Brunswick
(UNB), Canada. He is also a Canada Research Chair in Ad-
vanced Geomatics Image Processing, a Chang-Jiang Chair
Professor of Peking University, and a Visiting Professor of
the Massachusetts Institute of Technology (2015). Aditya Ro-
shan, Shabnam Jabari, Sina A. Khiabani, Fatemeh Fatholla-
hi, and Rakesh K. Mishra are Ph.D students and post-doctor-
al fellows in Yun Zhang’s research lab.
