Testing a New Approach for ASTER Image Data Sharpening via Using Diverse Principle Components

Image data sharpening is a widely used method to increase a spatial resolution of images with a higher spectral and lower spatial resolution. In our study we focused on sharpening ASTER image data using a high spatial-resolution panchromatic band of WorldView-2 data. Both datasets were acquired within the framework of a geological mapping project in the southwest Mongolia. Primary remote sensing task was to produce mineral maps for the studied area. ASTER data providing several bands in the short wave infrared (SWIR) spectral region has a great potential for geological/mineral mapping. On the other hand, a spatial resolution is rather coarse for the geological mapping at a 1: 50, 000 scale. ENVI and Erdas Imagine software (SW) were used to test the available Principle Component Analysis sharpening algorithm; however satisfactory spectral mapping results have not been achieved. In the commercial SW, the first component (PC) is used for the sharpening process by default, but the 1st PC usually does not contain the main spectral variability considering mineralogy/geology. Therefore, a new approach using the other principal components for image sharpening was tested and compared with the approach available in the commercial SW. New processing was programmed in ENVI/IDL.