Adaptive Chebyshev polynomial analysis for fusion of remote sensing vegetation imagery

This paper describes a novel approach of an adaptive fusion method by using Chebyshev polynomial analysis (CPA) for use in remote sensing vegetation imagery. Chebyshev polynomials have been effectively used in image fusion mainly in medium to high noise conditions, though its application is limited to heuristics. In this research, we have proposed a way to adaptively select the optimal CPA parameters according to user specifications. Performance evaluation affirms the approach's ability in reducing computational complexity for remote sensing images affected by noise.