Adaptive chebyshev fusion of vegetation imagery based on SVM classifier

Abstract

A novel approach of an adaptive fusion method by using Chebyshev polynomial analysis (CPA) for use in remote sensing vegetation imagery is described in this paper. Chebyshev polynomials have been effectively used in image fusion mainly in medium to high noise conditions, though its application was limited to heuristics. In this research, we have proposed a way to adaptively select the optimal CPA parameters according to user specifications. Support vector machines (SVM) is used as a classifying tool to estimate the noise parameters, from which the appropriate CPA degree is utilised to perform image fusion according to a look-up table. Performance evaluation affirms the approach’s ability in reducing computational complexity for remote sensing images affected by noise.