Abstract:
The extraction of information on land cover classes
using unsupervised methods has always been of relevance to the
remote sensing community. In this paper, a novel criterion is proposed,
which extracts the inherent information in an unsupervised
fashion from a time series. The criterion is used to fit a parametric
model to a time series, derive the corresponding covariance matrices
of the parameters for the model, and estimate the additive noise
on the time series. The proposed criterion uses both spatial and
temporal information when estimating the covariance matrices
and can be extended to incorporate spectral information. The
algorithm used to estimate the parameters for the model is the
extended Kalman filter (EKF). An unsupervised search algorithm,
specifically designed for this criterion, is proposed in conjunction
with the criterion that is used to rapidly and efficiently estimate the
variables. The search algorithm attempts to satisfy the criterion by
employing density adaptation to the current candidate system. The
application in this paper is the use of an EKF to model Moderate
Resolution Imaging Spectroradiometer time series with a triply
modulated cosine function as the underlying model. The results
show that the criterion improved the fit of the triply modulated
cosine function by an order of magnitude on the time series over
all seven spectral bands when compared with the other methods.
The state space variables derived from the EKF are then used for
both land cover classification and land cover change detection.
The method was evaluated in the Gauteng province of South
Africa where it was found to significantly improve on land cover
classification and change detection accuracies when compared
with other methods.
