plot overviews

Author: gilbertocamara

DESCRIPTION

@@ -28,10 +28,12 @@ Description: An end-to-end toolkit for land use and land cover classification
     temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, 
     residual networks by Fawaz et al (2019) <doi:10.1007/s10618-019-00619-1>, and temporal attention encoders
     by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>.
+    Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>.
     Performs efficient classification of big Earth observation data cubes and includes 
     functions for post-classification smoothing based on Bayesian inference, and 
-    methods for uncertainty assessment. Enables best
-    practices for estimating area and assessing accuracy of land change as 
+    methods for active learning and uncertainty assessment. Supports object-based 
+    time series analysis using package supercells <https://jakubnowosad.com/supercells/>.
+    Enables best practices for estimating area and assessing accuracy of land change as 
     recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>.
     Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.
 Encoding: UTF-8

NAMESPACE

@@ -274,8 +274,6 @@ S3method(.view_add_overlay_grps,class_cube)
 S3method(.view_add_overlay_grps,derived_cube)
 S3method(.view_add_overlay_grps,raster_cube)
 S3method(.view_add_overlay_grps,vector_cube)
-S3method(.view_adjust_palette,default)
-S3method(.view_adjust_palette,sar_cube)
 S3method(plot,class_cube)
 S3method(plot,class_vector_cube)
 S3method(plot,geo_distances)

R/api_gdal.R

@@ -92,22 +92,30 @@
     )
     return(invisible(file))
 }
-#' @title Run gdal_warp for SAR GRD files
+#' @title Run gdal_warp_file
 #' @noRd
 #' @param raster_file  File to be copied from (with path)
-#' @param size         Size of output file
+#' @param sizes        Sizes of output file
+#' @param t_srs        Target spatial reference system
 #' @returns            Name of output file
-.gdal_warp_grd <- function(raster_file, size) {
+.gdal_warp_file <- function(raster_file, sizes, t_srs = NULL) {
+    # create a temporary file
     temp_file <- tempfile(fileext = ".tif")
+    # basic parameters
+    params = list(
+        "-ts" = list(sizes[["xsize"]], sizes[["ysize"]]),
+        "-multi" = FALSE,
+        "-q" = TRUE,
+        "-overwrite" = FALSE
+    )
+    # additional param for target SRS
+    if (.has(t_srs))
+        params <- append(params, c("t_srs" = t_srs))
+    # warp the data
     .gdal_warp(
         file = temp_file,
         base_files = raster_file,
-        params = list(
-            "-ts" = list(size[["xsize"]], size[["ysize"]]),
-            "-multi" = FALSE,
-            "-q" = TRUE,
-            "-overwrite" = FALSE
-        ),
+        params = params,
         quiet = TRUE)
     return(temp_file)
 }

R/api_plot_raster.R

@@ -38,7 +38,7 @@
     band_offset <- .offset(band_conf)
     max_value <- .max_value(band_conf)
     # retrieve the overview if COG
-    bw_file <- .gdal_warp_grd(bw_file, sizes)
+    bw_file <- .gdal_warp_file(bw_file, sizes)
     # open the file in terra
     rast <- terra::rast(bw_file)
     # apply scale and offset
@@ -142,9 +142,9 @@
     max_value <- .max_value(band_params)
     # used for SAR images without tiling system
     if (tile[["tile"]] == "NoTilingSystem")  {
-        red_file   <- .gdal_warp_grd(red_file, sizes)
-        green_file <- .gdal_warp_grd(green_file, sizes)
-        blue_file  <- .gdal_warp_grd(blue_file, sizes)
+        red_file   <- .gdal_warp_file(red_file, sizes)
+        green_file <- .gdal_warp_file(green_file, sizes)
+        blue_file  <- .gdal_warp_file(blue_file, sizes)
     }
     # plot multitemporal band as RGB
     p <- .plot_rgb_stars(
@@ -201,9 +201,9 @@
     sizes <- .tile_overview_size(tile = tile, max_size)
     # used for SAR images
     if (tile[["tile"]] == "NoTilingSystem") {
-        red_file   <- .gdal_warp_grd(red_file, sizes)
-        green_file <- .gdal_warp_grd(green_file, sizes)
-        blue_file  <- .gdal_warp_grd(blue_file, sizes)
+        red_file   <- .gdal_warp_file(red_file, sizes)
+        green_file <- .gdal_warp_file(green_file, sizes)
+        blue_file  <- .gdal_warp_file(blue_file, sizes)
     }
     p <- .plot_rgb_stars(
         red_file = red_file,