twdtwClassify: Classify time series

Description

This function classifies the intervals of a time series based on the TWDTW results.

Usage

twdtwClassify(x, ...)
# S4 method for twdtwMatches
twdtwClassify(
  x,
  patterns.labels = NULL,
  from = NULL,
  to = NULL,
  by = NULL,
  breaks = NULL,
  overlap = 0.5,
  thresholds = Inf,
  fill = "unclassified"
)
# S4 method for twdtwRaster
twdtwClassify(
  x,
  patterns.labels = NULL,
  thresholds = Inf,
  fill = 255,
  filepath = "",
  ...
)

Value

An object of class twdtw*.

Arguments

x: An object of class twdtw*. This is the target time series. Usually, it is a set of unclassified time series.
...: Arguments to pass to specific methods for each twdtw* class and other arguments to pass to writeRaster and pbCreate.
patterns.labels: a vector with labels of the patterns.
from: A character or Dates object in the format "yyyy-mm-dd".
to: A character or Dates object in the format "yyyy-mm-dd".
by: A character with the interval size, e.g. "6 month".
breaks: A vector of class Dates. This replaces the arguments from, to, and by.
overlap: A number between 0 and 1. The minimum overlapping between one match and the interval of classification. Default is 0.5, i.e. an overlap minimum of 50%.
thresholds: A numeric vector the same length as patterns.labels. The TWDTW dissimilarity thresholds, i.e. the maximum TWDTW cost for consideration in the classification. Default is Inf for all patterns.labels.
fill: A character to fill the classification gaps. For signature twdtwTimeSeries the default is fill="unclassified", for signature twdtwRaster the default is fill="unclassified".
filepath: A character. The path at which to save the raster with results. If not provided the function saves in the same directory as the input time series raster.

Author

Victor Maus, vwmaus1@gmail.com

References

Maus:2019dtwSat

Maus:2016dtwSat

Examples

Run this code

if (FALSE) {
  
# Example of TWDTW analysis using raster files 
library(dtwSat)
library(caret) 

# Load raster data 
evi  <- brick(system.file("lucc_MT/data/evi.tif",  package = "dtwSat"))
ndvi <- brick(system.file("lucc_MT/data/ndvi.tif", package = "dtwSat"))
red  <- brick(system.file("lucc_MT/data/red.tif",  package = "dtwSat"))
blue <- brick(system.file("lucc_MT/data/blue.tif", package = "dtwSat"))
nir  <- brick(system.file("lucc_MT/data/nir.tif",  package = "dtwSat"))
mir  <- brick(system.file("lucc_MT/data/mir.tif",  package = "dtwSat"))
doy  <- brick(system.file("lucc_MT/data/doy.tif",  package = "dtwSat"))
timeline <- 
  scan(system.file("lucc_MT/data/timeline", package = "dtwSat"), what="date")

# Create raster time series 
rts <- twdtwRaster(evi, ndvi, red, blue, nir, mir, timeline = timeline, doy = doy)

# Load field samples and projection 
field_samples <- 
  read.csv(system.file("lucc_MT/data/samples.csv", package = "dtwSat"))
proj_str <- 
  scan(system.file("lucc_MT/data/samples_projection", package = "dtwSat"), 
       what = "character")

# Split samples for training (10%) and validation (90%) using stratified sampling 
set.seed(1)
I <- unlist(createDataPartition(field_samples$label, p = 0.1))
training_samples <- field_samples[I, ]
validation_samples <- field_samples[-I, ]

# Get time series form raster
training_ts <- getTimeSeries(rts, y = training_samples, proj4string = proj_str)
validation_ts <- getTimeSeries(rts, y = validation_samples, proj4string = proj_str)

# Create temporal patterns 
temporal_patterns <- createPatterns(training_ts, freq = 8, formula = y ~ s(x))

# Set TWDTW weight function 
log_fun <- logisticWeight(-0.1, 50)

# Run TWDTW analysis 
system.time(
  r_twdtw <-
    twdtwApply(x = rts, y = temporal_patterns, weight.fun = log_fun, progress = 'text') 
)

# Plot TWDTW distances for the first year 
plot(r_twdtw, type = "distance", time.levels = 1)

# Classify raster based on the TWDTW analysis 
r_lucc <- twdtwClassify(r_twdtw, progress = 'text')

# Plot TWDTW classification results 
plot(r_lucc, type = "map")

# Assess classification 
twdtw_assess <- 
  twdtwAssess(object = r_lucc, y = validation_samples, 
              proj4string = proj_str, conf.int = .95) 

# Plot map accuracy 
plot(twdtw_assess, type = "accuracy")

# Plot area uncertainty 
plot(twdtw_assess, type = "area")

# Plot misclassified samples  
plot(twdtw_assess, type = "map", samples = "incorrect") 

# Get latex table with error matrix 
twdtwXtable(twdtw_assess, table.type = "matrix")

# Get latex table with error accuracy 
twdtwXtable(twdtw_assess, table.type = "accuracy")

# Get latex table with area uncertainty 
twdtwXtable(twdtw_assess, table.type = "area")

}

if (FALSE) {
  
# Example of TWDTW analysis using raster files 
library(dtwSat)
library(caret) 

# Load raster data 
evi  <- brick(system.file("lucc_MT/data/evi.tif",  package = "dtwSat"))
ndvi <- brick(system.file("lucc_MT/data/ndvi.tif", package = "dtwSat"))
red  <- brick(system.file("lucc_MT/data/red.tif",  package = "dtwSat"))
blue <- brick(system.file("lucc_MT/data/blue.tif", package = "dtwSat"))
nir  <- brick(system.file("lucc_MT/data/nir.tif",  package = "dtwSat"))
mir  <- brick(system.file("lucc_MT/data/mir.tif",  package = "dtwSat"))
doy  <- brick(system.file("lucc_MT/data/doy.tif",  package = "dtwSat"))
timeline <- 
  scan(system.file("lucc_MT/data/timeline", package = "dtwSat"), what="date")

# Create raster time series 
rts <- twdtwRaster(evi, ndvi, red, blue, nir, mir, timeline = timeline, doy = doy)

# Load field samples and projection 
field_samples <- 
  read.csv(system.file("lucc_MT/data/samples.csv", package = "dtwSat"))
proj_str <- 
  scan(system.file("lucc_MT/data/samples_projection", package = "dtwSat"), 
       what = "character")

# Split samples for training (10%) and validation (90%) using stratified sampling 
set.seed(1)
I <- unlist(createDataPartition(field_samples$label, p = 0.1))
training_samples <- field_samples[I, ]
validation_samples <- field_samples[-I, ]

# Get time series form raster
training_ts <- getTimeSeries(rts, y = training_samples, proj4string = proj_str)
validation_ts <- getTimeSeries(rts, y = validation_samples, proj4string = proj_str)

# Create temporal patterns 
temporal_patterns <- createPatterns(training_ts, freq = 8, formula = y ~ s(x))

# Set TWDTW weight function 
log_fun <- logisticWeight(-0.1, 50)

# Run TWDTW analysis 
system.time(
  r_twdtw <-
    twdtwApply(x = rts, y = temporal_patterns, weight.fun = log_fun, progress = 'text') 
)

# Plot TWDTW distances for the first year 
plot(r_twdtw, type = "distance", time.levels = 1)

# Classify raster based on the TWDTW analysis 
r_lucc <- twdtwClassify(r_twdtw, progress = 'text')

# Plot TWDTW classification results 
plot(r_lucc, type = "map")

# Assess classification 
twdtw_assess <- 
  twdtwAssess(object = r_lucc, y = validation_samples, 
              proj4string = proj_str, conf.int = .95) 

# Plot map accuracy 
plot(twdtw_assess, type = "accuracy")

# Plot area uncertainty 
plot(twdtw_assess, type = "area")

# Plot misclassified samples  
plot(twdtw_assess, type = "map", samples = "incorrect") 

# Get latex table with error matrix 
twdtwXtable(twdtw_assess, table.type = "matrix")

# Get latex table with error accuracy 
twdtwXtable(twdtw_assess, table.type = "accuracy")

# Get latex table with area uncertainty 
twdtwXtable(twdtw_assess, table.type = "area")

}

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Description

Usage

Value

Arguments

Author

References

See Also

Examples