sits_segment: Segment an image

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

Segment an image

Description

Apply a spatial-temporal segmentation on a data cube based on a user defined segmentation function. The function applies the segmentation algorithm "seg_fn" to each tile. The output is a vector data cube, which is a data cube with an additional vector file in "geopackage" format.

Usage

sits_segment(
  cube,
  seg_fn = sits_slic(),
  roi = NULL,
  impute_fn = impute_linear(),
  start_date = NULL,
  end_date = NULL,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1",
  progress = TRUE
)

Arguments

cube	Regular data cube
seg_fn	Function to apply the segmentation
roi	Region of interest (see below)
impute_fn	Imputation function to remove NA values.
start_date	Start date for the segmentation
end_date	End date for the segmentation.
memsize	Memory available for classification (in GB).
multicores	Number of cores to be used for classification.
output_dir	Directory for output file.
version	Version of the output (for multiple segmentations).
progress	Show progress bar?

Value

A tibble of class 'segs_cube' representing the segmentation.

Note

Segmentation requires the following steps:

1. Create a regular data cube with sits_cube and sits_regularize;

2. Run sits_segment to obtain a vector data cube with polygons that define the boundary of the segments;

3. Classify the time series associated to the segments with sits_classify, to get obtain a vector probability cube;

4. Use sits_label_classification to label the vector probability cube;

5. Display the results with plot or sits_view.

The "roi" parameter defines a region of interest. It can be an sf_object, a shapefile, or a bounding box vector with named XY values ("xmin", "xmax", "ymin", "ymax") or named lat/long values ("lon_min", "lat_min", "lon_max", "lat_max").

As of version 1.5.3, the only seg_fn function available is sits_slic, which uses the Simple Linear Iterative Clustering (SLIC) algorithm that clusters pixels to generate compact, nearly uniform superpixels. This algorithm has been adapted by Nowosad and Stepinski to work with multispectral and multitemporal images. SLIC uses spectral similarity and proximity in the spectral and temporal space to segment the image into superpixels. Superpixels are clusters of pixels with similar spectral and temporal responses that are spatially close.

The result of sits_segment is a data cube tibble with an additional vector file in the geopackage format. The location of the vector file is included in the data cube tibble in a new column, called vector_info.

Author(s)

Gilberto Camara, [e-mail]

Rolf Simoes, [e-mail]

Felipe Carvalho, [e-mail]

Felipe Carlos, [e-mail]

References

Achanta, Radhakrishna, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk. 2012. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods.” IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11): 2274–82.

Nowosad, Jakub, and Tomasz F. Stepinski. 2022. “Extended SLIC Superpixels Algorithm for Applications to Non-Imagery Geospatial Rasters.” International Journal of Applied Earth Observation and Geoinformation 112 (August): 102935.

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_slic(
            step = 10,
            compactness = 1,
            dist_fun = "euclidean",
            avg_fun = "median",
            iter = 30,
            minarea = 10
        ),
        output_dir = tempdir()
    )
    # 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()
    )
    # label the probability segments
    seg_label <- sits_label_classification(
        cube = seg_probs,
        output_dir = tempdir()
    )
}

sits documentation built on Sept. 9, 2025, 5:54 p.m.