PIF: Pseudo-Invariant Features

In landsat: Radiometric and Topographic Correction of Satellite Imagery

Pseudo-Invariant Features

Description

Pseudo-invariant features identification for relative radiometric normalization.

Usage

PIF(band3, band4, band7, level = 0.99)

Arguments

band3	Landsat band 3, as a filename to be imported, a matrix, data frame, or SpatialGridDataFrame.
band4	Landsat band 4, as a filename to be imported, a matrix, data frame, or SpatialGridDataFrame.
band7	Landsat band 7, as a filename to be imported, a matrix, data frame, or SpatialGridDataFrame.
level	Threshold level for identifying PIFs. (0 < level < 1)

Details

Pseudo-invariant features (PIFs) are areas such as artificial structures that can reasonably be expected to have a constant reflectance over time, rather than varying seasonally as vegetation does. Differences in PIF reflectance between dates can be assumed to be due to varying atmospheric conditions.

Value

Returns a PIF mask in the same format as the input files, with 1 for pseudo-invariant features and 0 for background data.

Author(s)

Sarah Goslee

References

Schott, J. R.; Salvaggio, C. & Volchok, W. J. 1988. Radiometric scene normalization using pseudoinvariant features. Remote Sensing of Environment 26:1-16.

See Also

RCS

Examples

	# identify pseudo-invariant feature
	data(july3)
	data(july4)
	data(july7)
	july.pif <- PIF(july3, july4, july7)

	# use PIFs to related nov to july Landsat data for band 3
	# properly, would also remove cloudy areas first
	data(nov3)
	# use major axis regression: error in both x and y
	nov.correction <- lmodel2:::lmodel2(july3@data[july.pif@data[,1] == 1, 1] ~ 
	nov3@data[july.pif@data[,1] == 1, 1])$regression.results[2, 2:3]
	nov3.corrected <- nov3
	nov3.corrected@data[,1] <- nov3@data[,1] * nov.correction[2] + nov.correction[1]

landsat documentation built on Aug. 25, 2023, 1:07 a.m.