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Last data update: 2014.03.03

R: Identify pseudo-invariant features from a satellite scene

maskInvarFeatures

R Documentation

Identify pseudo-invariant features from a satellite scene

Description

Identify pseudo-invariant features from a satellite scene based on a vis, near infravis and short-wave infravis band.

Usage

## S4 method for signature 'Satellite'
maskInvarFeatures(x)

## S4 method for signature 'RasterStack'
maskInvarFeatures(x, quant = 0.01, id_vis = 1L,
  id_nir = 2L, id_swir = 3L)

## S4 method for signature 'RasterLayer'
maskInvarFeatures(x, nir, swir, quant = 0.01)

Arguments

`x`	A Satellite object or a `raster::RasterLayer` providing the sensor's vis band.
`quant`	A value v = [0...1] which is used to define the percentage threshold values (thv) for invariant features (nir/vis ratio < thv, swir band values > 1-thv).
`id_vis`	Index of the visible band.
`id_nir`	Index of the near infravis band.
`id_swir`	Index of the short-wave infravis band.
`nir`	A `raster::RasterLayer` containing the sensor's nir band.
`swir`	A `raster::RasterLayer` containing the sensor's swir band.

Details

Invariant features are identified as pixels which belong to the group of (i) the n lowest VIS/NIR ratios and of (ii) the highest n SWIR values. The value of n is given by the parameter quant = [0...1].

Value

If x is a Satellite object, a Satellite object with added layer;
if x is a raster::RasterLayer object, a a raster::RasterLayer object with added layers (1 indicates invariant pixels, 0 otherwise).

References

This function is taken and only slightly modified from the PIF function by Sarah C. Goslee (2011). Analyzing Remote Sensing Data in R: The landsat Package. Journal of Statistical Software,43(4), 1-25. URL http://www.jstatsoft.org/v43/i04/.

The underlying theory has been published by Schott RJ, Salvaggio C and Volchok WJ (1988) Radiometric scene normalization using pseudoinvariant features. Remote Sensing of Environment 26/1, doi:10.1016/0034-4257(88)90116-2, available online at http://www.sciencedirect.com/science/article/pii/0034425788901162.

Examples

path <- system.file("extdata", package = "satellite")
files <- list.files(path, pattern = glob2rx("LC8*.tif"), full.names = TRUE)
sat <- satellite(files)
sat <- maskInvarFeatures(sat)

maskInvarFeatures(x = getSatDataLayer(sat, "B004n"),
                  nir = getSatDataLayer(sat, "B005n"),
                  swir = getSatDataLayer(sat, "B007n"))

## when dealing with a 'RasterStack'
rst <- stack(files[c(6, 7, 9)])
maskInvarFeatures(rst)