RADCAL: RADCAL

In imad: Iteratively Re-weighted Multivariate Alteration Detection (iMAD)

Description

Perform automated radiometric normalization between two rasters following an iMAD calculations.

Usage

  RADCAL(inDataSet1, inDataSet2, chisqr_raster,
    noChangeProbThresh = 0.95, minNoChangePixels = NA,
    graph_only = TRUE, return_gains_and_offsets = TRUE,
    apply_to_raster = inDataSet2)

Arguments

inDataSet1	A Raster* object of the reference image.
inDataSet2	A Raster* object of the image to be normalized.
chisqr_raster	Raster. The chi-square image generated from iMad (e.g. raster(imad_output,layer=1))
noChangeProbThresh	Numeric. The probability threshold (0 <= noChangeProbThresh <= 1) for determining no change (default = 0.95).
minNoChangePixels	Logical. NOT SUPPORTED.
graph_only	Logical. NOT SUPPORTED.
return_gains_and_offsets	Logical. Return the gains and offsets as a matrix?
apply_to_raster	Raster*. The Raster* to apply the gains and the offsets to (default: inDataSet2). Set to NA if you don't want to apply the gains and offsets at this stage.

Value

List (if return_gains_and_offsets==TRUE && !is.na(apply_to_raster)) of the gains and offsets and normalized raster. Matrix of gains and rasters if !is.na(apply_to_raster). Raster* of the normalized image if return_gains_and_offsets==FALSE.

Author(s)

Mort Canty (original code) and Jonathan A. Greenberg (R port).

References

• Canty, M.J. and A.A. Nielsen. 2008. Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation. Remote Sensing of Environment 112:1025-1036.

• Nielsen, A.A. 2007. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Transactions on Image Processing 16(2):463-478.

imad documentation built on May 2, 2019, 6:05 p.m.