pca_fusion: Principal Components Analysis Transformation

In pacoalonso/fusionImage: This package implemente three image fusion algorithms (High Pass Filter, Principal Component Analysis, and Gram-Schmidt), and ERGAS fussion quality index

Description

Principal Components Analysis Transformation

Usage

pca_fusion(mis, pan, method, bits, matrix = NULL, mode = 1)

Arguments

mis	Raster brick object with the original multispectral bands
pan	Raster layer object with the panchromatic band
method	Resampling method, should be ‘"bilinear"’ for bilinear interpolation, or ‘"ngb"’
bits	Radiometric resolution of the original multispectral bands
matrix	PCA matrix. It is the pca matrix of variable loadings. The usual working mode is to allow the function calculates it, but it can be introduced by the user.
mode	With mode=-1 the first component is multiplied by -1

Value

Raster brick object with the bands obtained by the fusion process

Examples

pca <- pca_fusion(mis=mis, pan=pan, method="bilinear", bits=16)

pacoalonso/fusionImage documentation built on April 30, 2020, 4:26 a.m.