dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R

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published 86421 2 245165 document 86421 lightbox.jpg image/png 60a1aae3cc8f7c957ba5994eae77b069 MD5 32546 2019-01-31 08:48:37 https://pure.iiasa.ac.at/id/eprint/14513/8/lightbox.jpg 14513 8 8 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/86415 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/86415 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/86415 archive 353 disk0/00/01/45/13 2017-03-31 09:05:13 2021-08-27 17:28:48 2017-03-31 09:05:13 article show Maus V. 8780 0000-0002-7385-4723 Camara G. Appel M. Pebesma E. dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R pub prog_esm dynamic programming, MODIS time series, land cover changes, crop monitoring 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 lead to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for re mote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat . This package provides an implementation of the Time-Weighted Dynamic Time Warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant result s in time series analysis. dtwSat is available from the Comprehensive R Archive Network and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualising and assessing the results. 2019-01-29 published 3138 public Journal of Statistical Software 88 5 TRUE 1548-7660 https://cran.r-project.org/web/packages/dtwSat/index.html data http://pure.iiasa.ac.at/14514/ data FALSE no info:eu-repo/semantics/article