sits (version 1.5.3)

sits_mixture_model: Multiple endmember spectral mixture analysis

Description

Create a multiple endmember spectral mixture analyses fractions images. We use the non-negative least squares (NNLS) solver to calculate the fractions of each endmember. The NNLS was implemented by Jakob Schwalb-Willmann in RStoolbox package (licensed as GPL>=3).

Usage

sits_mixture_model(data, endmembers, ...)
# S3 method for sits
sits_mixture_model(
  data,
  endmembers,
  ...,
  rmse_band = TRUE,
  multicores = 2L,
  progress = TRUE
)
# S3 method for raster_cube
sits_mixture_model(
  data,
  endmembers,
  ...,
  rmse_band = TRUE,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  progress = TRUE
)
# S3 method for derived_cube
sits_mixture_model(data, endmembers, ...)
# S3 method for tbl_df
sits_mixture_model(data, endmembers, ...)
# S3 method for default
sits_mixture_model(data, endmembers, ...)

Value

In case of a cube, a sits cube with the fractions of each endmember will be returned. The sum of all fractions is restricted to 1 (scaled from 0 to 10000), corresponding to the abundance of the endmembers in the pixels. In case of a sits tibble, the time series will be returned with the values corresponding to each fraction.

Arguments

data

A sits data cube or a sits tibble.

endmembers

Reference spectral endmembers. (see details below).

...

Parameters for specific functions.

rmse_band

A boolean indicating whether the error associated with the linear model should be generated. If true, a new band with errors for each pixel is generated using the root mean square measure (RMSE). Default is TRUE.

multicores

Number of cores to be used for generate the mixture model.

progress

Show progress bar? Default is TRUE.

memsize

Memory available for the mixture model (in GB).

output_dir

Directory for output images.

Author

Felipe Carvalho,

Felipe Carlos,

Rolf Simoes,

References

RStoolbox package (https://github.com/bleutner/RStoolbox/)

Examples

if (sits_run_examples()) {
    # Create a sentinel-2 cube
    s2_cube <- sits_cube(
        source = "AWS",
        collection = "SENTINEL-2-L2A",
        tiles = "20LKP",
        bands = c("B02", "B03", "B04", "B8A", "B11", "B12", "CLOUD"),
        start_date = "2019-06-13",
        end_date = "2019-06-30"
    )
    # create a directory to store the regularized file
    reg_dir <- paste0(tempdir(), "/mix_model")
    dir.create(reg_dir)
    # Cube regularization for 16 days and 160 meters
    reg_cube <- sits_regularize(
        cube = s2_cube,
        period = "P16D",
        res = 160,
        roi = c(
            lon_min = -65.54870165,
            lat_min = -10.63479162,
            lon_max = -65.07629670,
            lat_max = -10.36046639
        ),
        multicores = 2,
        output_dir = reg_dir
    )

    # Create the endmembers tibble
    em <- tibble::tribble(
        ~class, ~B02, ~B03, ~B04, ~B8A, ~B11, ~B12,
        "forest", 0.02, 0.0352, 0.0189, 0.28, 0.134, 0.0546,
        "land", 0.04, 0.065, 0.07, 0.36, 0.35, 0.18,
        "water", 0.07, 0.11, 0.14, 0.085, 0.004, 0.0026
    )

    # Generate the mixture model
    mm <- sits_mixture_model(
        data = reg_cube,
        endmembers = em,
        memsize = 4,
        multicores = 2,
        output_dir = tempdir()
    )
}