In interpretation of remote sensing images, it is possible that some images which are supplied by different sensors become understandable. For better visual perception of these images, it is essential to operate series of pre-processing and elementary corrections and then operate a series of main processing steps for more precise analysis on the images. There are several approaches for processing which are depended on the type of remote sensing images. The discussed approach in this article, i.e. image fusion, is the use of natural colors of an optical image for adding color to a grayscale satellite image which gives us the ability for better observation of the HR image of OLI sensor of Landsat-8. This process with emphasis on details of fusion technique has previously been performed; however, we are going to apply the concept of the interpolation process. In fact, we see many important software tools such as ENVI and ERDAS as the most famous remote sensing image processing tools have only classical interpolation techniques (such as bi-linear (BL) and bi-cubic/cubic convolution (CC)). Therefore, ENVI-and ERDAS-based researches in image fusion area and even other fusion researches often don't use new and better interpolators and are mainly concentrated on the fusion algorithm's details for achieving a better quality, so we only focus on the interpolation impact on fusion quality in Landsat-8 multispectral images. The important feature of this approach is to use a statistical, adaptive, and edge-guided interpolation method for improving the color quality in the images in practice. Numerical simulations show selecting the suitable interpolation techniques in MRF-based images creates better quality than the classical interpolators.
For example in REF , LMMSE has been applied as a magnifier for achieving better quality in pan-sharpening process of Landsat-8 images compared to a linear interpolator.
MRF-Based Multispectral Image Fusion Using an Adaptive Approach Based on Edge-Guided Interpolation
Journal of Geographic Information System, vol. 9, no. 2, pp. 114-125 (2017)
Computer Science Geography