Optical Images Fusion Based on Linear Interpolation Methods

Abstract

Merging images is one of the most important technologies in remote sensing applications and geographic information systems. In this study, a simulation process using a camera for fused images by using resizing image for interpolation methods (nearest, bilinear and bicubic). Statistical techniques have been used as an efficient merging technique in the images integration process employing different models namely Local Mean Matching (LMM) and Regression Variable Substitution (RVS), and apply spatial frequency techniques include high pass filter additive method (HPFA).  Thus, in the current research, statistical measures have been used to check the quality of the merged images. This has been carried out by calculating the correlation and some traditional measures of the images before and after the integration process. Results showed that the adopted fusion process and statistical measures have efficiently and qualitatively determined the preference of images after the merge process and indicated which techniques are the best and estimation homogenous regions.