ootb_mblt: Out-of-the-box model-based local thresholding

In GastonMauroDiaz/rcaiman: CAnopy IMage ANalysis

Out-of-the-box model-based local thresholding

Description

Out-of-the-box version of the model-based local thresholding (MBLT) algorithm

Usage

ootb_mblt(r, z, a, bin, w = 0.5)

Arguments

r	SpatRaster. A normalized greyscale image. Typically, the blue channel extracted from a canopy photograph. Please see read_caim() and normalize_minmax().
z	SpatRaster built with zenith_image().
a	SpatRaster built with azimuth_image().
bin	SpatRaster. This should be a preliminary binarization of r useful for masking pixels that are very likely pure sky pixels.
w	Numeric vector of length one. Weighting parameter from \insertCiteDiaz2018;textualrcaiman's Equation 1. See thr_mblt()

Details

The MBLT approach proposes a linear relationship between background value and optimal threshold value. This function is a hard-coded version of a MBLT pipeline. It uses statistical models for sky reconstruction of the type able to explain smooth changes in sky brightness. Therefore, it works best under clear skies or overcast conditions. After the reconstruction, local thresholds are linearly predicted from sky brightness values (see thr_mblt()).

As a high-level summary, the pipeline starts with a working binarized image and ends with a refined binarized image. It combines these main functions extract_sky_points(), fit_coneshaped_model(), and fit_trend_surface(). The code can be easily inspected by calling ootb_mblt without parenthesis. Advanced users can use the code as a template.

The MBLT algorithm was first presented in \insertCiteDiaz2018;textualrcaiman. The version presented here differs from the original in the following main aspects:

• The original version used a global thresholding method to provide sky points to the cone-shaped model. This one uses extract_sky_points() as a way to improve the extraction of pure sky points from working binarized images.

• intercept and slope are automatically obtained using data from sky points and a linear model for accuracy evaluation after \insertCitePineiro2008;textualrcaiman. This approach handles inaccuracies in background reconstruction (see thr_mblt() for additional details).

• This version does not use asynchronous acquisition under the open sky, as the original method did. The cone-shaped model (fit_coneshaped_model()) run without a filling source and the cone-shaped sky is used as filling source for trend surface fitting (fit_trend_surface()).

This function searches for black objects against a light background. When regular canopy hemispherical images are provided as input, the algorithm will find dark canopy elements against a bright sky almost everywhere in the picture and, therefore, the result will fit user expectations. However, if a hemispherical photograph taken under the open sky is provided, this algorithm will be still searching for black objects against a light background, so the darker portions of the sky will be taken as objects, i.e., canopy. As a consequence, this will not fit users expectations since users will be looking for the classes Gap and No-gap, no matter if one of those are not in the picture itself. This kind of error could happen with photographs of open forests for the same working principle.

If you use this function in your research, please cite \insertCiteDiaz2018;textualrcaiman in addition to this package (citation("rcaiman")).

Value

Object from class list containing the binarized image (named bin) and the reconstructed skies (named sky_cs and sky_s).

Note

If NULL is provided as the w argument, the weight is calculated as the coefficient of determination (R^2) of the linear model for accuracy evaluation \insertCitePineiro2008rcaiman.

References

\insertAllCited

See Also

Other Binarization Functions: apply_thr(), obia(), ootb_obia(), regional_thresholding(), rem_isolated_black_pixels(), thr_isodata(), thr_mblt()

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)

## End(Not run)

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