R: Find matches between patterns and time series using TWDTW

sits_twdtw_classify {sits} | R Documentation

## Find matches between patterns and time series using TWDTW

### Description

Returns the results of the TWDTW matching function. The TWDTW matching function compares the values of a satellite image time series with the values of known patters and tries to match each pattern to a part of the time series

The TWDTW (time-weighted dynamical time warping) is a version of the Dynamic Time Warping method for LUCC mapping using a 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 results in time series analysis. In contrast to standard DTW, the TWDTW method is sensitive to seasonal changes of natural and cultivated vegetation types. It also considers inter-annual climatic and seasonal variability.

### Usage

    sits_twdtw_classify(
      samples,
      patterns,
      bands = NULL,
      dist_method = "euclidean",
      alpha = -0.1,
      beta = 100,
      theta = 0.5,
      span = 0,
      keep = FALSE,
      start_date = NULL,
      end_date = NULL,
      interval = "12 month",
      overlap = 0.5,
      .plot = TRUE
    )

### Arguments

`samples` | A sits tibble to be classified using TWTDW.
`patterns` | Patterns to be used for classification.
`bands` | Names of the bands to be used for classification.
`dist_method` | Name of the method to derive the local cost matrix.
`alpha` | Steepness of the logistic function used for temporal weighting (a double value).
`beta` | Midpoint (in days) of the logistic function.
`theta` | Relative weight of the time distance compared to the dtw distance.
`span` | Minimum number of days between two matches of the same pattern in the time series (approximate).
`keep` | Keep internal values for plotting matches?
`start_date` | Start date of the classification period.
`end_date` | End date of the classification period.
`interval` | Period between two classifications in months.
`overlap` | Minimum overlapping between one match and the interval of classification.
`.plot` | Plot the output?

### Value

A dtwSat S4 object with the matches.

### Author(s)

Victor Maus, vwmaus1@gmail.com

Gilberto Camara, gilberto.camara@inpe.br

### References

Maus V, Camara G, Cartaxo R, Sanchez A, Ramos F, Queiroz G (2016). A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8):3729-3739, August 2016. ISSN 1939-1404. doi:10.1109/JSTARS.2016.2517118.

### Examples

    ## Not run:
    # Retrieve the set of samples for the Mato Grosso region
    samples <- sits_select(samples_mt_6bands, bands = c("NDVI", "EVI"))

    # get a point and classify the point with the ml_model
    point <- sits_select(point_mt_6bands, bands = c("NDVI", "EVI"))

    # plot the series
    plot(point)

    # obtain a set of patterns for these samples
    patterns <- sits_patterns(samples)
    plot(patterns)

    # find the matches between the patterns and the time series
    # using the TWDTW algorithm
    # (uses the dtwSat R package)
    matches <- sits_twdtw_classify(point, patterns,
        bands = c("NDVI", "EVI"),
        alpha = -0.1, beta = 100, theta = 0.5, keep = TRUE
    )

    ## End(Not run)

[Package sits version 0.14.1 Index]