Local features of SAR images for pixel classification

Computes moving-window statistics around every pixel, in the format used by DivergenceClassifier and the divergence_classifier() 'parsnip' model. Window statistics are computed with integral images, so the cost does not depend on the window size. Windows are truncated at the image borders.

Usage

sar_features(image, window = 3, type = c("all", "gaussian", "wishart"))

Arguments

image

A SARImage or an array accepted by SARImage$new().

window

Odd window side.

type

Which features: "all", "gaussian" or "wishart".

Value

A tibble with the columns row, col and the features.

Details

Two groups of columns are produced:

Gaussian features

mean_<ch>: local mean of each intensity channel; cov_<ch1>_<ch2>: local (unbiased) covariance between the intensities. Together they define the multivariate normal fitted to the intensity vector in the window.

Wishart features (PolSAR only)

c<i><i> and c<i><j>_re/c<i><j>_im: entries of the local mean covariance matrix, i.e. the maximum likelihood estimate of \(\Sigma\) for the scaled complex Wishart law in the window.

Examples

img <- SARImage$new(SanFrancisco150)$crop(1:20, 1:20)
f <- sar_features(img, window = 3)
names(f)
#>  [1] "row"       "col"       "mean_HH"   "mean_HV"   "mean_VV"   "cov_HH_HH"
#>  [7] "cov_HH_HV" "cov_HH_VV" "cov_HV_HV" "cov_HV_VV" "cov_VV_VV" "c11"      
#> [13] "c22"       "c33"       "c12_re"    "c12_im"    "c13_re"    "c13_im"   
#> [19] "c23_re"    "c23_im"