agenbagFilters {imagine}

Performs algorithms from Agenbag et al. (2003)

Description

This function performs two (gradient) calculation approaches for SST, as outlined in the paper by Agenbag et al. (2003).

Usage

agenbagFilters(X, algorithm = c(1, 2), ...)

Arguments

X

A numeric matrix used as main input.

algorithm

integer specifying the type of method that will be used. See Details.

...

Not used.

Details

Section 2.2.4 of the paper by Agenbag et al. (2003) introduces the following two methods:

Method 1:

Based on the equation

Y_{i,j}=\sqrt{(X_{i+1,j}-X_{i-1,j})^2 +(X_{i,j+1}-X_{i,j-1})^2}

where Y_{i,j} represents the output value for each X_{i,j} pixel value of a given X matrix.

Method 2:

the standard deviation in a 3x3 pixel area centered on position (i,j).

As outlined in the original study, this method conducts searches within a 1-pixel vicinity of each point. For method 1, it only returns a value for points where none of the four involved values are NA. Conversely, for method 2, the standard deviation calculation is performed only for points where at least 3 non-NA values are found in the 3x3 neighborhood.

Value

agenbagFilters returns a matrix object with the same dimensions of X.

References

Agenbag, J.J., A.J. Richardson, H. Demarcq, P. Freon, S. Weeks, and F.A. Shillington. "Estimating Environmental Preferences of South African Pelagic Fish Species Using Catch Size- and Remote Sensing Data". Progress in Oceanography 59, No 2-3 (October 2003): 275-300. (doi:10.1016/j.pocean.2003.07.004).

Examples

data(wbImage)

# Agenbag, method 1
agenbag1 <- agenbagFilters(X = wbImage, algorithm = 1)

# Agenbag, method 2
agenbag2 <- agenbagFilters(X = wbImage, algorithm = 2)

# Plotting results
par(mfrow = c(3, 1), mar = rep(0, 4))

# Original
image(wbImage, axes = FALSE, col = gray.colors(n = 1e3))

# Calculated
cols <- hcl.colors(n = 1e3, palette = "YlOrRd", rev = TRUE)
image(agenbag1, axes = FALSE, col = cols)
image(agenbag2, axes = FALSE, col = cols)