Year-end Sale % OFF 
Abstract
Once the satellite sensor is in orbit, no hardware enhancement of the lens assembly can be done to improve spatial and spectral resolution. Super resolution (SR) as single frame or multi-frame can solve this problem up to a large extent. In this study, single- and multi-frame SR techniques were applied and tested on Worldview-2 datasets as well as on across-spatial datasets of LISS III and LISS IV. Study of soft classifier’s behavior on super-resolved images was performed through possibilistic C-means classifier. Quantitative methods based on calculation of peak signal-to-noise ratio, mean square error, root means square error, image quality index and qualitative methods of visual interpretation proved that both super-resolution methods remove outliers in an efficient way and resulted in images containing sharp edges. The single-frame super-resolution technique was found relatively inferior in terms of contrast and spatial resolution. Overall, multi-frame SR method outperformed other methods.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
