Detecting land cover change by evaluating the internal covariance matrix of the Extended Kalman Filter

In this paper, the internal operations of an Extended Kalman Filter is investigated to see if any useful information can be derived to detect land cover change in a MODIS time series. The Extended Kalman Filter expands its internal covariance if a significant change in reflectance value is observed, followed by adapting the state parameters to compensate for this change. The analysis shows a change detection accuracy above 90% can be attained when evaluating the elements within the internal covariance matrix to detect new human settlements, with a corresponding false alarm rate below 11%.