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A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping

Authors: Victor Maus; Gilberto Camara; Ricardo Cartaxo; Alber Sanchez; Fernando M. Ramos; Gilberto Ribeiro de Queiroz;

A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping

Abstract

This paper presents a time-weighted version of the dynamic time warping (DTW) method for land-use and land-cover classification using remote sensing image time series. Methods based on DTW have achieved significant results in time-series data mining. The original DTW method works well for shape matching, but is not suited for remote sensing time-series classification. It disregards the temporal range when finding the best alignment between two time series. Since each land-cover class has a specific phenological cycle, a good time-series land-cover classifier needs to balance between shape matching and temporal alignment. To that end, we adjusted the original DTW method to include a temporal weight that accounts for seasonality of land-cover types. The resulting algorithm improves on previous methods for land-cover classification using DTW. In a case study in a tropical forest area, our proposed logistic time-weighted version achieves the best overall accuracy of 87.32%. The accuracy of a version with maximum time delay constraints is 84.66%. A time-warping method without time constraints has a 70.14% accuracy. To get good results with the proposed algorithm, the spatial and temporal resolutions of the data should capture the properties of the landscape. The pattern samples should also represent well the temporal variation of land cover.

Related Organizations
Subjects by Vocabulary

Microsoft Academic Graph classification: Dynamic time warping Land use Computer science Land cover Tropical forest computer.software_genre Shape matching Data mining Time series Classifier (UML) computer

Keywords

Atmospheric Science, Computers in Earth Sciences

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    120
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
120
Top 1%
Top 10%
Top 1%
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