Abstract
In this letter, the application of superresolution (SR) techniques to multispectral image clustering and classification is investigated and tested using satellite data. A set of multispectral images with better spatial resolution is obtained after an SR technique is applied to several data sets recorded within a short period over a study area. Improved clustering and classification performance is demonstrated visually and quantitatively by comparison with the original low-resolution data or enlarged images using a conventional interpolation method. This letter illustrates the possibility and feasibility of the use of SR reconstruction for the classification of remote sensing data, which is encouraging as a means of breaking through current satellite detectors' resolution limits.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.