IEEE Geoscience and Remote Sensing Magazine - December 2020 - 37

NCC and MSD better serves as the similarity metric when
compared to a statistical measure like MI. It is a useful feature that could be added to the image registration tool box
and employed for better data analysis of current as well as
future payloads' data.
Currently, many of the processing tasks of remote sensing data are carried out onboard the payloads. This is possible with the advent of specialized hardware for space applications. It is observed that generally faster approaches
requiring less memory and storage space and fewer computations are preferred for the onboard implementations.
One can see that the registration software developed for
KALPANA-1 and INSAT-3A provides better performance
with the help of least-squares surface fitting for optimization with a combination of NCC and MSD as the similarity metric. It is evident that such a simple but effective
algorithm could be a natural choice for possible onboard
hardware implementation.
Finally, this software could also be an obvious choice
when developing a multistage registration software where
the final registration is achieved by combining different
state-of-the-art algorithms. The methodology can also be
adopted for other modalities of remote sensing data.
ACKNOWLEDGMENTS
We thank the editor, associate editor, and two anonymous
reviewers for their insightful comments. We are grateful to
the Department of Space, Government of India, for allowing
us to carry out the work discussed in this article at the Space
Applications Centre (SAC) of the Indian Space Research
Organization (ISRO) and making available the Very High
Resolution Radiometer/Charge-Coupled Device sensors
data sets from KALPANA-1 and INSAT-3A payloads. We are
thankful to Mr. Tapan Misra, former director, SAC (ISRO),
Ahmedabad, for allowing us to publish the work. We thank
Dr. R. Ramakrishanan, group head, Data Products Software
Group, for his encouragement and support. Our thanks go
to the director general, Indian Meteorological Department,
New Delhi, for providing vital statistics for both the payloads. Special thanks to Mr. Sajid Mohammad, SAC (ISRO),
and Dr. Bhargab Chattopadhyay, Indian Institute of Management Visakhapatanam, for their valuable contributions
in the preparation of this paper.
AUTHOR INFORMATION
Jignesh S. Bhatt (jignesh.bhatt@iiitvadodara.ac.in) is with
the Indian Institute of Information Technology Vadodara,
Gandhinagar, India. He has authored the books Regularization in Hyperspectral Unmixing (SPIE Press, 2016) and Unmixing Approaches in Hyperspectral Remote Sensing (CRC Press, to
be published).
Narayan Padmanabhan (n.padmanabhan@ahduni.edu
.in) is with the School of Engineering and Applied Science,
Ahmedabad University, India. Previously, he was a scientist
at the Space Applications Centre of the Indian Space Research Organization, Ahmedabad.
DECEMBER 2020

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

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