demo/dl_comparison.R

In sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

devAskNewPage(ask = FALSE)

# load the sits library
library(sits)
if (!requireNamespace("sitsdata", quietly = TRUE)) {
    stop(
        paste0(
            "Please install package sitsdata\n",
            "Please call devtools::install_github('e-sensing/sitsdata')"
        ),
        call. = FALSE
    )
}

# load the sitsdata library
library(sitsdata)

# load a dataset of time series samples for the Mato Grosso region
data("samples_matogrosso_mod13q1")
# create a list to store the results
results <- list()
# Deep Learning - MLP

print("== Accuracy Assessment = DL =======================")
acc_ltae <- sits_kfold_validate(samples_matogrosso_mod13q1,
    folds = 5,
    ml_method = sits_lighttae()
)
acc_ltae$name <- "LightTAE"

results[[length(results) + 1]] <- acc_ltae

# Deep Learning - TempCNN
print("== Accuracy Assessment = TempCNN =======================")
acc_tc <- sits_kfold_validate(samples_matogrosso_mod13q1,
    folds = 5,
    ml_method = sits_tempcnn()
)
acc_tc$name <- "TempCNN"

results[[length(results) + 1]] <- acc_tc

# Deep Learning - ResNet
print("== Accuracy Assessment = ResNet =======================")
acc_rn <- sits_kfold_validate(samples_matogrosso_mod13q1,
    folds = 5,
    ml_method = sits_resnet()
)
acc_rn$name <- "ResNet"

results[[length(results) + 1]] <- acc_rn

sits_to_xlsx(results, file = paste0(tempdir(), "/accuracy_mato_grosso_dl.xlsx"))