eCite Digital Repository

Improving features used for land cover change detection by reducing the uncertainty in the feature extraction method


Salmon, BP and Kleynhans, W and Olivier, JC and Schwegmann, CP, Improving features used for land cover change detection by reducing the uncertainty in the feature extraction method, Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, 23-28 July 2017, Texas, United State, pp. 1-4. ISBN 9781509049516 (2017) [Refereed Conference Paper]

PDF (author accepted version)

Copyright Statement

Copyright 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Official URL:


The well-being of the environment is one of the major factors that contributes to sustainability. Sustainable human settlements require local governance to plan, implement, develop, and manage human settlements expansions. This is important as the number anthropogenic activities is directly correlated to the increase in human population within a geographical region. Regional mapping of land cover conversion of natural vegetation to new human settlements is essential. In this paper we explore the effect which the length of a temporal sliding window has on the success of detecting land cover change. It is shown using a short Fourier transform as a feature extraction method provides meaningful robust input to a machine learning method. In theory, the performance is increased by improving the estimates on the features by increasing the length of the sliding window. Experiments were conducted in the Limpopo province of South Africa and were found that increasing the length of the sliding window beyond 12 months yield minor improves due to other seasonal and external factors.

Item Details

Item Type:Refereed Conference Paper
Keywords:change detection, fourier transform, satellite, 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:124431
Year Published:2017
Deposited By:Engineering
Deposited On:2018-02-21
Last Modified:2018-06-18
Downloads:40 View Download Statistics

Repository Staff Only: item control page