Land-cover separability analysis of MODIS time-series data using a combined simple harmonic oscillator and a mean reverting stochastic process

Abstract

It is proposed that the time series extracted from moderate resolution imaging spectroradiometer satellite data be modeled as a simple harmonic oscillator with additive colored noise. The colored noise ismodeled with an Ornstein–Uhlenbeck process. The Fourier transform and maximum-likelihood parameter estimation are used to estimate the harmonic and noise parameters of the colored simple harmonic oscillator. Two case studies in South Africa show that reliable class differentiation can be obtained between natural vegetation and settlement land cover types, when using the parameters of the colored simple harmonic oscillator as input features to a classifier. The two case studies were conducted in the Gauteng and Limpopo provinces of South Africa. In the case of the Gauteng case study, we obtained an average for single-band classification, while standard harmonic features only achieved an average. In conclusion, the results obtained from the colored simple harmonic oscillator approach outperformed standard harmonic features and the minimum distance classifier.