pifMatch | R Documentation |
Match one scene to another based on linear regression of pseudo-invariant features (PIF).
pifMatch( img, ref, method = "cor", quantile = 0.95, returnPifMap = TRUE, returnSimMap = TRUE, returnModels = FALSE )
img |
RasterStack or RasterBrick. Image to be adjusted. |
ref |
RasterStack or RasterBrick. Reference image. |
method |
Method to calculate pixel similarity. Options: euclidean distance ('ed'), spectral angle ('sam') or pearson correlation coefficient ('cor'). |
quantile |
Numeric. Threshold quantile used to identify PIFs |
returnPifMap |
Logical. Return a binary raster map ot pixels which were identified as pesudo-invariant features. |
returnSimMap |
Logical. Return the similarity map as well |
returnModels |
Logical. Return the linear models along with the adjusted image. |
The function consists of three main steps:
First, it calculates pixel-wise similarity between the two rasters and identifies pseudo-invariant pixels based on
a similarity threshold.
In the second step the values of the pseudo-invariant pixels are regressed against each other in a linear model for each layer.
Finally the linear models are applied to all pixels in the img
, thereby matching it to the reference scene.
Pixel-wise similarity can be calculated using one of three methods: euclidean distance (method = "ed"
), spectral angle ("sam"
) or pearsons correlation coefficient ("cor"
).
The threshold is defined as a similarity quantile. Setting quantile=0.95
will select all pixels with a similarity above the 95% quantile as pseudo-invariant features.
Model fitting is performed with simple linear models (lm
); fitting one model per layer.
Returns a List with the adjusted image and intermediate products (if requested). #'
img
: the adjusted image
simMap
: pixel-wise similarity map (if returnSimMap = TRUE
)
pifMap
: binary map of pixels selected as pseudo-invariant features (if returnPifMap = TRUE
)
models
: list of linear models; one per layer (if returnModels = TRUE
)
library(raster) ## Import Landsat example data data(lsat) ## Create fake example data ## In practice this would be an image from another acquisition date lsat_b <- log(lsat) ## Run pifMatch and return similarity layer, invariant features mask and models lsat_b_adj <- pifMatch(lsat_b, lsat, returnPifMap = TRUE, returnSimMap = TRUE, returnModels = TRUE) ## Pixelwise similarity ggR(lsat_b_adj$simMap, geom_raster = TRUE) ## Pesudo invariant feature mask ggR(lsat_b_adj$pifMap) ## Histograms of changes par(mfrow=c(1,3)) hist(lsat_b[[1]], main = "lsat_b") hist(lsat[[1]], main = "reference") hist(lsat_b_adj$img[[1]], main = "lsat_b adjusted") ## Model summary for first band summary(lsat_b_adj$models[[1]])
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