IMAGE FUSION ANALYSIS FOR HYPERSPECTRAL DATA

Y.Sai Praveen, A.Kiranmai, Iyyanki. V. Murali Krishna, K.Nikitha

Abstract: The hyperspectral sensors provide the images with hundreds of narrow contiguous spectral channels which provide plenty of spectral information and with this information; we can achieve high accuracy in classification problems and detection or identification of targets. However, the hyperspectral images are high in spectral resolution but low in spatial resolution. So in order to enhance the image with both spectral and spatial resolution, we support image fusion. In this paper, we have done image fusion along with preprocessing techniques such as radiometric and atmospheric corrections. The high dimensionality of the hyperspectral images may increase the computational complexity. We applied a dimensionality reduction technique that is Principal component analysis to reduce the number of bands that we consider for processing, and then performed the Image sharpening to enhance the spatial resolution. So the resulting will be high in both spectral and spatial resolution, and analysis of these image fusion techniques is also studied.

Keywords: Dimensionality Reduction, Hyperspectral, Principal Component Analysis,, Minimum Noise Fraction, Image Fusion, Gram Schmidt Pan sharpening.

DOI: https://doi.org/10.15623/ijret.2016.0519004