sits (version 1.5.4)

sits_snic: Segment an image using SNIC

Description

Apply a segmentation on a data cube based on the snic package. This is an adaptation and extension to remote sensing data of the SNIC superpixels algorithm proposed by Achanta and Süsstrunk (2017). See reference for more details.

Usage

sits_snic(
  data = NULL,
  grid_seeding = "rectangular",
  spacing = 10,
  compactness = 0.5,
  padding = floor(spacing/2)
)

Arguments

data

A matrix with time series.

grid_seeding

Method for grid seeding (one of "rectangular", "diamond", "hexagonal", "random").

spacing

Distance (in number of cells) between initial supercells' centers

compactness

A compactness value. Larger values cause clusters to be more compact/even (square).

padding

Distance (in pixels) from the image borders within which no seeds are placed.

Author

Rolf Simoes,

Gilberto Camara,

Felipe Carlos,

Felipe Carvalho,

References

"Superpixels and Polygons Using Simple Non-Iterative Clustering", R. Achanta and S. Süsstrunk, CVPR 2017.

Examples

if (sits_run_examples()) {
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    # create a data cube
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = data_dir
    )
    # segment the vector cube
    segments <- sits_segment(
        cube = cube,
        seg_fn = sits_snic(
            grid_seeding = "rectangular",
            spacing = 10,
            compactness = 0.5,
            padding = 5
        ),
        output_dir = tempdir(),
        version = "snic-demo"
    )
    # create a classification model
    rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
    # classify the segments
    seg_probs <- sits_classify(
        data = segments,
        ml_model = rfor_model,
        output_dir = tempdir(),
        version = "snic-demo"
    )
    # label the probability segments
    seg_label <- sits_label_classification(
        cube = seg_probs,
        output_dir = tempdir(),
        version = "snic-demo"
    )
    plot(seg_label)
}