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Multiple Endmember Spectral Mixture Analysis (Spectral Unmixing)

Source: R/mesma.R
mesma.Rd

mesma performs a spectral mixture anlylsis (SMA) or multiple endmember spectral mixture analysis (MESMA) on a multiband raster image.

Usage

mesma(
  img,
  em,
  method = "NNLS",
  iterate = 400,
  tolerance = 1e-08,
  n_models = NULL,
  sum_to_one = TRUE,
  ...,
  verbose
)

Arguments

img

SpatRaster. Remote sensing imagery (usually hyperspectral).

em

Matrix or data.frame with spectral endmembers. Columns represent the spectral bands (i.e. columns correspond to number of bands in img). Rows represent either a single endmember per class (SMA) or multiple endmembers per class (MESMA), if a column with name class is present, containing the class name each endmember belongs to, e.g. "water" or "land". See details below. Number of rows needs to be > 1.

method

Character. Select an unmixing method. Currently, only "NNLS" is implemented. Default is "NNLS".

  • NNLS: applies a non-negative least squares (NNLS) regression which is using a sequential coordinate-wise algorithm (SCA) based on Franc et al. (2005).

iterate

Integer. Set maximum iteration per pixel. Processing time could increase the more iterations are made possible. Default is 400.

tolerance

Numeric. Tolerance limit representing a nearly zero minimal number. Default is 1e-8.

n_models

Logical. Only applies if em contains column class. Defines how many endmember combinations should be picked. Maximum is the number of possible endmember combinations (see details). Defaults to the maximum possible endmember combinations calculated from em.

sum_to_one

Logical. Defines whether a sum-to-one constraint should be applied so that probabilities of endmember classes sum to one (a constraint not covered by NNLS) to be interpretable as fractions. Defaults to TRUE. To get actual NNLS results, change to FALSE.

...

further arguments passed to writeRaster.

verbose

Logical. Prints progress messages during execution.

Value

SpatRaster. The object will contain one band per class, with each value representing the estimated probability of the respective endmember class per pixel, and an RMSE band. If sum_to_one is TRUE (default), values of the class bands can be interpreted as fractions per endmember class (0 to 1).

Details

Argument em determines whether an SMA (each row representing a single endmember per class) or a MESMA (multiple endmembers per class differentiate using the class column) is computed. If multiple endmembers per class are provided, mesma will compute a number of SMA (determined by argument n_models) for multiple selected endmember combinations drawn from em and will select the best fit per pixel based on the lowest RMSE, based on the MESMA approach proposed by Roberts et al. (1998). For this, mesma computes all possible combinations of endmembers in em, than uses all of them unless a lower number of endmember combinations is set through argument n_models. n_models cannot be larger than the maximum number of possible endmember combinations in em. mesma always uses those combinations where the number of unique endmembers used is maximal first.

Note

Depending on iterate and tolerance settings and the selected endmembers, the sum of estimated probabilities per pixel varies around 1. NNLS does not account for a sum-to-one constraint. Use sum_to_one to sum to one post-NNLS.

To achieve best results, it is recommended to adjust n_models in accordance to the number of endemembers per class provided through em so that as many endmember combinations as possible (with each endmember being used once) are computed. The more models are being calculated, the more processing and memory recourses are needed.

References

Franc, V., Hlaváč, V., & Navara, M. (2005). Sequential coordinate-wise algorithm for the non-negative least squares problem. In: International Conference on Computer Analysis of Images and Patterns (pp. 407-414). Berlin, Heidelberg.

Roberts, D. A., Gardner, M., Church, R., Ustin, S., Scheer, G., & Green, R. O. (1998). Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote sensing of environment, 65(3), 267-279.

Author

Jakob Schwalb-Willmann

Examples


library(RStoolbox)
library(terra)

#  to perform a SMA, use a single endmember per class, row by row:
em <- data.frame(lsat[c(5294, 47916)])
rownames(em) <- c("forest", "water")

# umix the lsat image
probs <- mesma(img = lsat, em = em)
plot(probs)


# to perform a MESMA, use multiple endmembers per class, differntiating them
# by a column named 'class':
if (FALSE) { # \dontrun{
em <- rbind(
  data.frame(lsat[c(4155, 17018, 53134, 69487, 83704)], class = "forest"),
  data.frame(lsat[c(22742, 25946, 38617, 59632, 67313)], class = "water")
)

# unmix the lsat image
probs <- mesma(img = lsat, em = em)
plot(probs)

# MESMA can also be performed on more than two endmember classes:
em <- rbind(
  data.frame(lsat[c(4155, 17018, 53134, 69487, 83704)], class = "forest"),
  data.frame(lsat[c(22742, 25946, 38617, 59632, 67313)], class = "water"),
  data.frame(lsat[c(4330, 1762, 1278, 1357, 17414)], class = "shortgrown")
)

# unmix the lsat image
probs <- mesma(img = lsat, em = em)
plot(probs)
} # }