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Unsupervised land cover change estimation using region covariance estimates

Citation

Olding, WC and Olivier, JC and Salmon, BP and Kleynhans, W, Unsupervised land cover change estimation using region covariance estimates, IEEE Geoscience and Remote Sensing Letters, 16, (3) pp. 347-351. ISSN 1545-598X (2019) [Refereed Article]

Copyright Statement

Copyright 2018 IEEE

DOI: doi:10.1109/LGRS.2018.2875974

Abstract

In this letter, we demonstrate the utility of estimating a probabilistic model of the underlying seasonal and interannual variations experienced by land cover time series in a given geographical region. Time series that deviate from these trajectories due to the human-induced change appear as outliers and can be detected using their Mahalanobis distance from the mean under the joint distribution of time samples. We apply this model to a collection of pixel time series acquired by the Moderate Resolution Imaging Spectroradiometer platform over Limpopo province, South Africa, for the task of identifying human settlement expansion. For estimation of the time of change, we present a hypothesis testing approach that tests for a decrease in correlation between samples before and after the change. This was found to be highly effective, yielding a mean absolute error of 52 days.

Item Details

Item Type:Refereed Article
Keywords:change detection algorithms, covariance matrices, density estimation robust algorithm, remote sensing, time series analysis
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Circuits and Systems
Objective Division:Environment
Objective Group:Environmental Policy, Legislation and Standards
Objective Field:Environmental Policy, Legislation and Standards not elsewhere classified
UTAS Author:Olding, WC (Mr Willem Olding)
UTAS Author:Olivier, JC (Professor JC Olivier)
UTAS Author:Salmon, BP (Dr Brian Salmon)
ID Code:129021
Year Published:2019
Deposited By:Engineering
Deposited On:2018-11-02
Last Modified:2020-01-14
Downloads:0

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