Open AccessEffect of Land Control Points Spatial Allocation for the Image Registration of Remote Sensing Images
Author(s) -
Harshlata Vishwakarma,
Sunil Kumar Katiyar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1608.078219
Subject(s) - remote sensing , computer science , voronoi diagram , satellite , control point , image registration , computer vision , point (geometry) , geospatial analysis , homography , polygon (computer graphics) , distribution (mathematics) , image resolution , representation (politics) , artificial intelligence , geography , frame (networking) , image (mathematics) , mathematics , statistics , geometry , telecommunications , mathematical analysis , projective test , politics , projective space , law , political science , engineering , aerospace engineering
With the development in space technology, new remote sensing satellites are launched around the world tremendously. The high resolution camera gives high resolution satellite images and the large data is produced by remote sensors persistently. Because of high efficiency, the vast inclusion of data with not being restricted by the spatial parameter, satellite representation winds up one of the imperative way to obtain geospatial data. With this data, the obtaining of land control points is essential in the image registration and geometric improvement of satellite pictures. In this research work, the influence of the quantity and geographical distribution of land ground control point in image registration and accurateness is examine through Voronoi Diagram (Thiessen polygon). A simulation investigation was carried out using remote sensing pictures to analyze the impact of distributed patterns of land control points on image registration and correction. The corrected values are measured by square root mean error (SRME) and with residual separations. It exhibits that the center distribution gives the most reduced SRME. Additionally, demonstrates that the land control point distribution in the center of image is less distorted in comparison to land control points positioned at borders and corners. Subsequently, the centralized uniform distribution of ground control points shows better results taking into consideration the overall deformation rate on the complete image registration.