mesma: Multiple Endmember Spectral Mixture Analysis (Spectral...

In rspatial/luna: Tools for Satellite Remote Sensing (Earth Observation) Data Processing

Multiple Endmember Spectral Mixture Analysis (Spectral Unmixing)

Description

mesma performs a multiple endmember spectral mixture analysis on a multiband raster image.

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

Usage

## S4 method for signature 'SpatRaster'
mesma(x, em, iterate=400, tolerance=0.00000001, ...)

Arguments

x	SpatRaster. Usually representing a hyperspectral remotely sensed image
em	matrix or data.frame with spectral endmembers. Rows represent a single endmember of a class, columns represent the spectral bands (i.e. columns correspond to number of bands in img). The number of rows needs to be > 1
iterate	integer. Set maximum iteration per pixel. Processing time could increase the more iterations are made possible
tolerance	numeric. Tolerance limit representing a nearly zero minimal number
...	further arguments passed to writeRaster

Value

SpatRaster with one layer per end-member, with each value representing the estimated presence probability of the end-member per pixel (0 to 1), and an RMSE layer.

Note

Depending on iterate and tolerance settings, the sum of estimated presence probabilities per pixel varies around 1.

Author(s)

Jakob Schwalb-Willmann

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.

Examples

lsat <- rast(system.file("ex/lsat.tif", package="luna"))

# endmember spectra: water and land
em_names <- c("water", "land")
pts <- data.frame(class=em_names, cell = c(47916,5294))
em <- lsat[pts$cell]
rownames(em) <- em_names

# unmix the image for water and land
probs <- mesma(lsat, em)

# take a look
hist(probs$water)
plot(probs$water, col = c("white","blue"))
hist(probs$land)
plot(probs$land, col = c("white","brown"))

rspatial/luna documentation built on Feb. 3, 2024, 4:40 a.m.