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 is modeled 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.
