defuzzify: Defuzzify a fuzzy classification

In GastonMauroDiaz/rcaiman: CAnopy IMage ANalysis

Defuzzify a fuzzy classification

Description

This function translates degree of membership into Boolean logic using a regional approach. The output ensures that the fuzzy and Boolean versions remain consistent at the specified level of aggregation (controlled by the argument segmentation). This method makes perfect sense to translate a subpixel classification of gap fraction (or a linear ratio) into a binary product.

Usage

defuzzify(mem, segmentation)

Arguments

mem	A SpatRaster object representing the degree of membership in a fuzzy classification.
segmentation	An object of the class SpatRaster such as an object returned by sky_grid_segmentation().

Value

An object of the class SpatRaster containing binary information.

Note

This method is also available in the HSP software package \insertCiteLang2013rcaiman.

References

\insertAllCited

See Also

Other Tool Functions: calc_oor_index(), calc_spherical_distance(), colorfulness(), correct_vignetting(), display_caim(), extract_dn(), extract_feature(), extract_rel_radiance(), extract_sky_points(), extract_sun_coord(), find_sky_pixels(), masking(), optim_dist_to_black(), optim_normalize(), percentage_of_clipped_highlights(), read_bin(), read_caim(), read_caim_raw(), read_ootb_sky_model(), sor_filter(), vicinity_filter(), write_bin(), write_caim(), write_ootb_sky_model()

Examples

## Not run: 
path <- system.file("external/DSCN4500.JPG", package = "rcaiman")
caim <- read_caim(path, c(1250, 1020) - 745, 745 * 2, 745 * 2)
z <- zenith_image(ncol(caim), lens("Nikon_FCE9"))
a <- azimuth_image(z)
r <- gbc(caim$Blue)
r <- correct_vignetting(r, z, c(0.0638, -0.101)) %>% normalize_minmax()
bin <- find_sky_pixels(r, z, a)
bin <- ootb_mblt(r, z, a, bin)
plot(bin$bin)
ratio <- r / bin$sky_s
ratio <- normalize_minmax(ratio, 0, 1, TRUE)
plot(ratio)
g <- sky_grid_segmentation(z, a, 10)
bin2 <- defuzzify(ratio, g)
plot(bin2)
plot(abs(bin$bin - bin2))

## End(Not run)

GastonMauroDiaz/rcaiman documentation built on April 14, 2025, 9:39 a.m.