Humans are transforming land cover at an ever-increasing rate. Accurate geographical maps on land cover, especially rural and urban settlements are essential to planning sustainable development. Time series extracted from MODerate resolution Imaging Spectroradiometer (MODIS) land surface reflectance products have been used to differentiate land cover classes by analyzing the seasonal patterns in reflectance. In our study area we have successfully fitted a triply modulated cosine function to these seasonal patterns. We previously developed a meta-optimization approach for setting the parameters of the non-linear Extended Kalman Filter (EKF) to efficiently estimate the model variables for these triply modulated cosine functions using spatial information. In this paper we modify this approach to utilize temporal information instead of spatial information. This significantly reduces the processing time and storage requirements to process each time series. The features extracted using the proposed method are classified with a support vector machine and the performance of the method is compared to the original approach on our ground truth data.

Item Type:	Refereed Article
Keywords:	kalman filtering, remote sensing, satellites, time series
Research Division:	Engineering
Research Group:	Communications engineering
Research Field:	Signal processing
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in engineering
UTAS Author:	Salmon, BP (Dr Brian Salmon)
UTAS Author:	Olivier, JC (Professor JC Olivier)
ID Code:	125452
Year Published:	2018
Web of Science® Times Cited:	4
Deposited By:	Engineering
Deposited On:	2018-04-18
Last Modified:	2018-09-06
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