Abstract:
In this paper, a first order MODIS time series
simulator, which uses a Colored Simple Harmonic Oscillator, is
proposed. The simulated data can be used to augment data sets so
that data intensive classification and change detection algorithms
can be applied without enlarging the available ground truth data
sets. The simulator’s validity is tested by simulating data sets of
natural vegetation and human settlement areas and comparing it
to the ground truth data in the Gauteng province located in South
Africa. The difference found between the real and simulated
data sets, which is reported in the experiments is negligent. The
simulated and real world data sets are compared by using a wide
selection of class and pixel metrics. In particular the average
temporal Hellinger distance between the real and simulated data
sets is 0.2364 and 0.2269 for the vegetation and settlement class
respectively, while the average parameter Hellinger distance is
0.1835 and 0.2554 respectively.
