Hyperspectral Image Sharpening Using Fusion Techniques -A Case Study at Salah Al-Din Province/Iraq-

Abstract

The data fusion process includes merging two or more pieces of information obtained from different sensors. Satellite image fusion research aims to create a new image by combining two images captured by different sensors using various methodologies. In this research, image sharpening tools were used to combine a hyperspectral image with a low spatial resolution captured by a Hyperion sensor mounted on the Earth Observation 1 (EO-1) satellite with a grayscale high spatial resolution image captured by Enhanced Thematic Mapper Plus (ETM +) sensor mounted on Landsat-8 (resampling first one to ensure equal spatial resolution of both images). In addition, three techniques were adopted for implementing the Fusion mechanism: the Principal Component Analysis PCA, the Nearest Neighbor Diffusion NNDifuse, and the Gram-Schmidt method; these were used to sharpen hyperspectral data using high spatial resolution. The result showed that the Gram-Schmidt method could give Hyperspectral images with higher spectral and spatial resolution in panchromatic image data more accurately than the other methods.