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dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis
Provides a robust approach to land use mapping using multi-dimensional (multi-band) satellite image time series. By leveraging the Time-Weighted Dynamic Time Warping (TWDTW) distance metric in tandem with a 1 Nearest-Neighbor (1-NN) Classifier, this package offers functions to produce land use maps based on distinct seasonality patterns, commonly observed in the phenological cycles of vegetation. The approach is described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and Maus et al. (2019) <doi:10.18637/jss.v088.i05>. A primary advantage of TWDTW is its capability to handle irregularly sampled and noisy time series, while also requiring minimal training sets. The package includes tools for training the 1-NN-TWDTW model, visualizing temporal patterns, producing land use maps, and visualizing the results.
Getting started
Browse package contents
Package details |
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| Author | Victor Maus [aut, cre] (<https://orcid.org/0000-0002-7385-4723>), Marius Appel [ctb] (<https://orcid.org/0000-0001-5281-3896>), Nikolas Kuschnig [ctb] (<https://orcid.org/0000-0002-6642-2543>), Toni Giorgino [ctb] (<https://orcid.org/0000-0002-6642-2543>) |
| Maintainer | Victor Maus <vwmaus1@gmail.com> |
| License | GPL (>= 3) |
| Version | 1.0.0 |
| URL | https://github.com/vwmaus/dtwSat/ |
| Package repository | View on CRAN |
| Installation |
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