Abstract
This paper focuses on image matching between satellite imagery and aerial photographs of agriculture land. Feature points are used for image matching. The satellite imagery and aerial photographs were acquired at different times, viewpoints, sensors, and altitudes. Therefore, they possess very high temporal, photometric, and projective differences. When feature points, such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) are applied on such images, they demonstrate poor performance. This paper aims at evaluating the performance of SIFT, SURF, and other state of the art feature points in order to determine well deserving feature points for the images of agriculture land. We also propose a new feature point descriptor, i.e. Modified Normalized Gradient SIFT, which obtains on average 1.73–2.37% better performance than other state of the art descriptors.
Sign-in/Register to access full text options 
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.
