Merge changes

R-package/DESCRIPTION

 Package: HaSa
 Title: Autonomous Image Sampling and Probability Mapping
-Version: 1.3.0
+Version: 1.3.1
 Authors@R: 
     person(given = "Carsten",
            family = "Neumann",

R-package/R/inner_procedure.r

@@ -65,7 +65,13 @@ sample_nb <- function(raster,
   ###
   n_channel <- length(names(raster))
   ###raster in MEMORY or NOT
-  if (in.memory == TRUE && raster::fromDisk(raster) == TRUE) { rast <- raster::readAll(raster) }else { rast <-raster }
+  if (in.memory == TRUE &&
+      raster::fromDisk(raster) == TRUE) {
+    rast <- raster::readAll(raster)
+  } else {
+    rast <- raster
+  }
+  rast <- raster::mask(rast, raster::calc(rast, fun = sum))
   ###
   l <- 1        ###6. opt=260
   pbtn1 <- matrix(1, nrow = 1, ncol = 1)
@@ -107,7 +113,8 @@ sample_nb <- function(raster,
       res <- parallel::mclapply(
         1:nb_mean,
         model_opt_r,
-        raster = raster,
+        raster = rast,
+        col_names = names(raster),
         sample_type = sample_type,
         buffer = buffer,
         model = model,
@@ -119,7 +126,6 @@ sample_nb <- function(raster,
         mtry = mtry,
         mod.error = mod.error,
         pbtn1 = pbtn1,
-        rast = rast,
         max_samples_per_class = max_samples_per_class,
         mc.cores = cores,
         mc.preschedule = TRUE,
@@ -133,7 +139,8 @@ sample_nb <- function(raster,
     } else {
       for (k in 1:nb_mean) {
         res <- model_opt_r(
-          raster = raster,
+          raster = rast,
+          col_names = names(raster),
           sample_type = sample_type,
           buffer = buffer,
           model = model,
@@ -146,7 +153,6 @@ sample_nb <- function(raster,
           mtry = mtry,
           mod.error = mod.error,
           pbtn1 = pbtn1,
-          rast = rast,
           max_samples_per_class = max_samples_per_class
         )
         points_list[[k]] <- res$points

R-package/R/model_opt.r

@@ -4,6 +4,7 @@
 #'
 #' @param k Iteration value for the models.
 #' @param raster satellite time series stack (rasterBrickObject) or just any type of image (*rasterObject)
+#' @param col_names layer names
 #' @param sample_type distribution of spatial locations c("random","regular")
 #' @param buffer distance (in m) for new sample collection around initial samples (depends on pixel size)
 #' @param model which machine learning classifier to use c("rf", "svm") for random forest or support vector machine implementation
@@ -15,7 +16,6 @@
 #' @param mtry number of predictor used at random forest splitting nodes (mtry << n predictors)
 #' @param mod.error threshold for model error until which iteration is being executed
 #' @param pbtn1 matrix for points
-#' @param rast raster
 #' @param max_samples_per_class maximum number of samples per class
 #' 
 #' @return a list with 4 elements:
@@ -26,6 +26,7 @@
 #' @keywords internal
 model_opt_r <- function(k,
                         raster,
+                        col_names,
                         sample_type,
                         buffer,
                         model,
@@ -37,7 +38,6 @@ model_opt_r <- function(k,
                         mtry,
                         mod.error,
                         pbtn1,
-                        rast,
                         max_samples_per_class) {
   points <- NULL
   models <- NULL
@@ -49,24 +49,21 @@ model_opt_r <- function(k,
       if (sample_type == "random") {
         set.seed(seed2[k])
         pbt <-
-          raster::sampleRandom(raster, size = sample_size, sp = T)
+          raster::sampleRandom(rast, size = sample_size, sp = T)
       }
       if (sample_type == "regular") {
-        pbt <- raster::sampleRegular(raster, size = sample_size, sp = T)
+        pbt <- raster::sampleRegular(rast, size = sample_size, sp = T)
       }
-            
      f <- which(is.na(pbt@data[1]))
      if (length(f) != 0) {
        pbt <- pbt[-f,]
      }
-     
       set.seed(seed2[k])
       classes <-
         as.factor(sample(c(1:2), size = nrow(pbt), replace = T))
       if (length(levels(classes)) < 2) {
         break
       }
-      
       data <- as.data.frame(cbind(classes, pbt@data))
     }
     ########################################################################
@@ -153,16 +150,15 @@ model_opt_r <- function(k,
                                     rast))
       nam <-
         as.vector(fasterize::fasterize(sf::st_as_sf(poly), rast[[1]], field = "V1"))[-attr(test1, "na.action")]
-        
-      co <-
-        raster::xyFromCell(rast, c(1:raster::ncell(rast))[-attr(test1, "na.action")])
+      
+      co <- raster::xyFromCell(rast, c(1:raster::ncell(rast))[-attr(test1,"na.action")])
       pbtn1 <- as.data.frame(cbind(nam, co))
       sp::coordinates(pbtn1) <- c("x", "y")
                  
      if (ncol(test1) == 1) {
         test1 <- t(test1)
       }
-      colnames(test1) <- names(raster)
+      colnames(test1) <- col_names
           }
     if (class(test1)[1] == "numeric") {
       test1 <- t(matrix(test1))
@@ -189,7 +185,6 @@ model_opt_r <- function(k,
     #####################################
     ###maximum sample size
     if (sum(pbtn1@data$nam == 1) > max_samples_per_class) {
-      
       set.seed(seed)
       dr <-
         sample(which(pbtn1@data$nam == 1),

R-package/man/model_opt_r.Rd

@@ -7,6 +7,7 @@
 model_opt_r(
   k,
   raster,
+  col_names,
   sample_type,
   buffer,
   model,
@@ -18,7 +19,6 @@ model_opt_r(
   mtry,
   mod.error,
   pbtn1,
-  rast,
   max_samples_per_class
 )
 }
@@ -27,6 +27,8 @@ model_opt_r(
 
 \item{raster}{satellite time series stack (rasterBrickObject) or just any type of image (*rasterObject)}
 
+\item{col_names}{layer names}
+
 \item{sample_type}{distribution of spatial locations c("random","regular")}
 
 \item{buffer}{distance (in m) for new sample collection around initial samples (depends on pixel size)}
@@ -49,8 +51,6 @@ model_opt_r(
 
 \item{pbtn1}{matrix for points}
 
-\item{rast}{raster}
-
 \item{max_samples_per_class}{maximum number of samples per class}
 }
 \value{