Merge branch 'master' of https://github.com/e-sensing/sits into dev

Conflicts:
	R/sits_assessment.R

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: sits
 Type: Package
 Title: Satellite Image Time Series Analysis
-Version: 0.9.2
-Date: 2017-09-02
+Version: 0.9.17
+Date: 2017-09-17
 Authors@R: c(person('Gilberto', 'Camara', role = c('aut', 'cre'), email = '[email protected]'), 
  person('Rolf', 'Simoes', role = c('aut'), email = '[email protected]'),
  person('Victor', 'Maus', role = c('aut'), email = '[email protected]'),
@@ -25,8 +25,6 @@ BugReports: https://github.com/e-sensing/sits/issues
 License: GPL-2 | file LICENSE
 LazyData: true
 Imports:
- wtss,
- dtwSat,
  magrittr,
  caret,
  dendextend,
@@ -58,6 +56,7 @@ Imports:
  reshape2,
  R.utils,
  scales,
+ sf,
  signal,
  snow,
  sp,
@@ -68,7 +67,9 @@ Imports:
  utils,
  wtsps,
  TSdist,
- zoo
+ zoo,
+ wtss,
+ dtwSat
 LinkingTo:
  Rcpp
 Remotes:
@@ -83,10 +84,11 @@ Collate:
  'sits_TWDTW_plot.R'
  'sits_WTSPS.R'
  'sits_WTSS.R'
- 'sits_assessment.R'
+ 'sits_accuracy.R'
  'sits_classification.R'
  'sits_cluster.R'
  'sits_colors.R'
+ 'sits_confusion_matrix.R'
  'sits_dendrogram.R'
  'sits_distances.R'
  'sits_export.R'
@@ -101,10 +103,13 @@ Collate:
  'sits_patterns.R'
  'sits_pipe.R'
  'sits_plot.R'
+ 'sits_projections.R'
  'sits_raster.R'
+ 'sits_space_time_operations.R'
  'sits_stack_proc.R'
  'sits_tibble.R'
  'sits_tibble_utils.R'
+ 'sits_validate.R'
 Suggests: knitr,
  rmarkdown,
  testthat

NAMESPACE

@@ -2,19 +2,23 @@
 
 export("%>%")
 export("sits_cluster_names<-")
+export(sits_STRaster)
 export(sits_TS_distances)
 export(sits_TWDTW_classify)
 export(sits_TWDTW_distances)
 export(sits_TWDTW_matches)
+export(sits_XY_inside_raster)
 export(sits_accuracy)
 export(sits_accuracy_area)
 export(sits_accuracy_classif)
 export(sits_algorithmWTSPS)
 export(sits_align)
 export(sits_apply)
 export(sits_bands)
+export(sits_break_ts)
 export(sits_cancelProcess_WTSPS)
 export(sits_classify)
+export(sits_classify_raster)
 export(sits_cloud_filter)
 export(sits_cluster)
 export(sits_cluster_cleaner)
@@ -42,11 +46,12 @@ export(sits_formula_smooth)
 export(sits_fromCSV)
 export(sits_fromGZ)
 export(sits_fromJSON)
+export(sits_fromLatLong)
+export(sits_fromRaster)
 export(sits_fromSHP)
+export(sits_fromTable)
 export(sits_fromWTSS)
 export(sits_fromZOO)
-export(sits_fromlatlong)
-export(sits_fromtable)
 export(sits_gam)
 export(sits_gbm)
 export(sits_getcovWTSS)
@@ -63,8 +68,10 @@ export(sits_kfold_validate)
 export(sits_kohonen)
 export(sits_labels)
 export(sits_labels_list)
+export(sits_latlong_to_proj)
 export(sits_lda)
 export(sits_linear_interp)
+export(sits_match_dates)
 export(sits_max_colors)
 export(sits_merge)
 export(sits_missing_values)
@@ -83,7 +90,6 @@ export(sits_plot_patterns)
 export(sits_predict)
 export(sits_prune)
 export(sits_qda)
-export(sits_raster)
 export(sits_relabel)
 export(sits_rename)
 export(sits_rfor)
@@ -110,6 +116,8 @@ export(sits_toXLSX)
 export(sits_toZOO)
 export(sits_train)
 export(sits_transmute)
+export(sits_ts_fromRasterCSV)
+export(sits_ts_fromRasterXY)
 export(sits_values)
 export(sits_whittaker)
 import(dtwSat)

R/sits_accuracy.R

@@ -0,0 +1,163 @@
+#' @title Evaluates the accuracy of classification
+#' @name sits_accuracy
+#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br}
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#
+#' @description Evaluates the accuracy of classification stored in two vectors.
+#' Returns a confusion matrix used by the "caret" package
+#'
+#' @param conf.tb A tibble containing pairs of reference and predicted values
+#' @param conv.lst A list conversion list of labels. If NULL no conversion is done.
+#' @param pred_sans_ext (Boolean) remove all label extension (i.e. every string after last '.' character) from predictors before compute assesment.
+#' @return caret_assess a confusion matrix assessment produced by the caret package
+#'
+#' @export
+sits_accuracy <- function(conf.tb, conv.lst = NULL, pred_sans_ext = FALSE){
+
+
+ # recover predicted and reference vectors from input
+ pred.vec <- conf.tb$predicted
+ ref.vec <- conf.tb$reference
+
+ # remove predicted labels' extensions
+ if (pred_sans_ext)
+ pred.vec <- tools::file_path_sans_ext(pred.vec)
+
+ # convert class names
+ if (!purrr::is_null(conv.lst)) {
+ names_ref <- dplyr::pull (dplyr::distinct (conf.tb, reference))
+ ensurer::ensure_that(names_ref,
+ all(. %in% names(conv.lst)),
+ err_desc = "sits_accuracy: conversion list does not contain all reference labels")
+ pred.vec <- as.character(conv.lst[pred.vec])
+ ref.vec <- as.character(conv.lst[ref.vec])
+ }
+
+ # call caret package to the classification statistics
+ caret_assess <- caret::confusionMatrix(pred.vec, ref.vec)
+
+ # print the result
+ .print_confusion_matrix (caret_assess)
+
+ # return invisible
+ return (invisible(caret_assess))
+}
+#' @title Area-weighted post-classification accuracy assessment of classified maps
+#' @name sits_accuracy_area
+#' @author Victor Maus, \email{vwmaus1@@gmail.com}
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#'
+#' @description To use this function, the input table should be a set of results containing
+#' both the label assigned by the user and the classification result.
+#' Accuracy assessment set us a confusion matrix to determine the accuracy of your classified result.
+#' This function uses an area-weighted technique proposed by Olofsson et al. to
+#' produce accuracy estimates that are more reliable
+#'
+#' We plan to do an improved version of this function that includes a Raster R object
+#' with the classified map and a vector with the labels of the classified map
+#' (Gilberto-Rolf-05-Jun-2017)
+#'
+#' This function calls \code{\link[dtwSat]{twdtwAssess}} from \pkg{dtwSat}.
+#' \code{\link[dtwSat]{twdtwAssess}} performs an accuracy assessment of the classified, including
+#' Overall Accuracy, User's Accuracy, Produce's Accuracy, error matrix (confusion matrix),
+#' and estimated area according to [1-2].
+#'
+#' @references
+#' [1] Olofsson, P., Foody, G.M., Stehman, S.V., Woodcock, C.E. (2013).
+#' Making better use of accuracy data in land change studies: Estimating
+#' accuracy and area and quantifying uncertainty using stratified estimation.
+#' Remote Sensing of Environment, 129, pp.122-131.
+#'
+#' @references
+#' [2] Olofsson, P., Foody G.M., Herold M., Stehman, S.V., Woodcock, C.E., Wulder, M.A. (2014)
+#' Good practices for estimating area and assessing accuracy of land change. Remote Sensing of
+#' Environment, 148, pp. 42-57.
+#'
+#' @param results.tb a sits table with a set of lat/long/time locations with known and trusted labels and
+#' with the result of classification method
+#' @param area a list with the area of each label
+#' @param conf.int specifies the confidence level (0-1).
+#' @param rm.nosample if sum of columns and sum of rows of the error matrix are zero
+#' then remove class. Default is TRUE.
+#' @export
+sits_accuracy_area <- function (results.tb, area, conf.int = 0.95, rm.nosample = FALSE){
+
+ # Get reference classes
+ references <- results.tb$label
+
+ # Get mapped classes
+ # mapped <- dplyr::bind_rows(results.tb$distances) %>%
+ # dplyr::select(dplyr::matches("classification")) %>% unlist
+
+ # create a vector to store the result of the predictions
+ mapped <- results.tb$class
+ # Get all labels
+ classes <- unique(c(references, mapped))
+
+ # Create error matrix
+ error_matrix <- table(factor(mapped, levels = classes, labels = classes),
+ factor(references, levels = classes, labels = classes))
+
+ # Get area - TO IMPROVE USING THE METADATA FROM SATELLITE PRODUCTS
+ if(missing(area))
+ area <- rowSums(error_matrix)
+
+ # Compute accuracy metrics using dtwSat::twdtwAssess
+ assessment <- dtwSat::twdtwAssess (error_matrix,
+ area = area,
+ conf.int = conf.int,
+ rm.nosample = rm.nosample )
+
+ return (assessment)
+
+}
+
+#' @title Evaluates the accuracy of a labelled set of data
+#' @name sits_accuracy_classif
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#
+#' @description Tests the accuracy of a classification model by comparing an input data set
+#' that has been obtained independently to a the predicted values of the model.
+#' This function can be used to test the accuracy of a classification model against a
+#' data set that is obtained independently. The quality of the accuracy assessment
+#' depends critically of the quality of the input data set, which should be be part of the
+#' data set used for training the model.
+#' This function should be used when the patterns are not directly derived from the samples.
+#' It provides an initial assessment of the validity of using this set of pattern
+#' to classify an area whose samples are given.
+#' This function returns the Overall Accuracy, User's Accuracy,
+#' Producer's Accuracy, error matrix (confusion matrix), and Kappa values.
+#'
+#' @param data.tb A sits tibble containing a set of samples with known and trusted labels
+#' @param patterns.tb A sits tibble containing a set of patterns (independent of input data)
+#' @param ml_model A model trained by \code{\link[sits]{sits_train}}
+#' @param dist_method Method to compute distances (e.g., sits_TWDTW_distances)
+#' @param interval Period between two classifications
+#' @param ... Other parameters to be passed to the distance function
+#' @return assess Assessment of validation
+#' @export
+sits_accuracy_classif <- function (data.tb,
+ patterns.tb,
+ ml_model,
+ dist_method = sits_TWDTW_distances(),
+ interval = "12 month") {
+
+ # does the input data exist?
+ .sits_test_tibble (data.tb)
+ .sits_test_tibble (patterns.tb)
+ ensurer::ensure_that (data.tb, !("NoClass" %in% sits_labels(.)),
+ err_desc = "sits_test_patterns: please provide a labelled set of time series")
+
+ # classify data
+ class.tb <- sits_classify (data.tb, patterns.tb, ml_model, dist_method, interval = interval)
+ # retrieve the reference labels
+ class.tb <- dplyr::mutate (class.tb, reference = label)
+
+ # calculate the accuracy assessment
+ assess <- sits_accuracy(class.tb, pred_sans_ext = TRUE)
+
+ return (invisible (assess))
+}
+
+
+