dtwSat - Time-Weighted Dynamic Time Warping for satellite image time series analysis

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The opening of large archives of satellite data such as Landsat, MODIS and the Sentinels has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyse such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. The package dtwSat provides an implementation of the Time-Weighted Dynamic Time Warping (TWDTW) method for land cover mapping using multi-band satellite image time series (Maus et al. 2016). Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis (Velichko and Zagoruyko 1970; Hiroaki Sakoe and Chiba 1971; H. Sakoe and Chiba 1978; Rabiner and Juang 1993; Berndt and Clifford 1994; Keogh and Ratanamahatana 2005; Müller 2007). dtwSat is also available from the Comprehensive R Archive Network (CRAN). dtwSat provides full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualising, and assessing the results. Bellow we show a quick demo of the package usage. Fig. 1. Example time series which we want to classify.

https://github.com/vwmaus/dtwSat

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