landsat (version 1.1.2)

PIF: Pseudo-Invariant Features

Description

Pseudo-invariant features identification for relative radiometric normalization.

Usage

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

Value

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

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)

Author

Sarah Goslee

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.

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]