Description Usage Arguments Examples
View source: R/auto_classify.R
First the image should be preprocessed using the auto_preprocess
function. For Landsat CDR imagery, predictor layers can be generated using
the auto_generate_predictors
function.
1 2 | auto_classify(predictor_file, train_shp, output_path, class_col = "Poly_Type",
training = 0.6, overwrite = FALSE, notify = print)
|
predictor_file |
a |
train_shp |
a file readable by readOGR with training polygons |
output_path |
the path to use for the output |
class_col |
the name of the column containing the response variable (for example the land cover type of each pixel) |
training |
indicator of which polygons to use in training. Can be: 1) a string giving the name of a column indicating whether each polygon is to be used in training (column equal to TRUE) or in testing (column equal to FALSE), or 2) a logical vector of length equal to length(polys), or 3) a number between 0 and 1 indicating the fraction of the polygons to be randomly selected for use in training. |
overwrite |
whether to overwrite existing files (otherwise an error will be raised) |
notify |
notifier to use (defaults to |
1 | #TODO: Add example
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.