otbTexturesHaralick: OTB wrapper for Haralick's simple, advanced and higher order...

In environmentalinformatics-marburg/satelliteTools: What the package does (short line)

Description

OTB wrapper for calculating Haralick's simple, advanced and higher order texture features on every pixel in each channel of the input image.

Usage

## S4 method for signature 'RasterBrick'
otbTexturesHaralick(x, texture = "all",
  path_output = NULL, return_raster = TRUE, parameters.xyrad = list(c(1,
  1)), parameters.xyoff = list(c(1, 1)), parameters.minmax = c(0, 255),
  parameters.nbbin = 8, channel = NULL, verbose = FALSE, ram = "8192")

## S4 method for signature 'RasterLayer'
otbTexturesHaralick(x, texture = "all",
  path_output = NULL, return_raster = TRUE, parameters.xyrad = list(c(1,
  1)), parameters.xyoff = list(c(1, 1)), parameters.minmax = c(0, 255),
  parameters.nbbin = 8, channel = NULL, verbose = FALSE, ram = "8192")

## S4 method for signature 'RasterStack'
otbTexturesHaralick(x, texture = "all",
  path_output = NULL, return_raster = TRUE, parameters.xyrad = list(c(1,
  1)), parameters.xyoff = list(c(1, 1)), parameters.minmax = c(0, 255),
  parameters.nbbin = 8, channel = NULL, verbose = FALSE, ram = "8192")

## S4 method for signature 'character'
otbTexturesHaralick(x, texture = "all",
  output_name = "hara", path_output = NULL, return_raster = FALSE,
  parameters.xyrad = list(c(1, 1)), parameters.xyoff = list(c(1, 1)),
  parameters.minmax = c(0, 255), parameters.nbbin = 8, channel = NULL,
  verbose = FALSE, ram = "8192")

Arguments

x	A Raster* object or a GeoTiff containing one or more gray value bands
texture	type of filter "all" for all, alternative one of "simple" "advanced" "higher"
return_raster	boolean if TRUE a raster stack is returned
parameters.xyrad	list with the x and y radius in pixel indicating the kernel sizes for which the textures are calculated
parameters.xyoff	vector containg the directional offsets. Valid combinations are: list(c(1,1),c(1,0),c(0,1),c(1,-1))
parameters.minmax	minimum/maximum gray value which can occur.
parameters.nbbin	number of gray level bins (classes)
channel	sequence of bands to be processed
verbose	switch for system messages default is FALSE
ram	reserved memory in MB
output_name	string pattern vor individual naming of the output file(s)
n_grey	Number of grey values.
parallel	A logical value indicating whether parameters are calculated parallely or not

Details

"simple":
computes the following 8 local Haralick textures features: Energy, Entropy, Correlation, Inverse Difference Moment, Inertia, Cluster Shade, Cluster Prominence and Haralick Correlation. They are provided in this exact order in the output image. Thus, this application computes the following Haralick textures over a neighborhood with user defined radius.
To improve the speed of computation, a variant of Grey Level Co-occurrence Matrix(GLCM) called Grey Level Co-occurrence Indexed List (GLCIL) is used. Given below is the mathematical explanation on the computation of each textures. Here g( i,j) is the frequency of element in the GLCIL whose index is i,j. GLCIL stores a pair of frequency of two pixels from the given offset and the cell index (i,j) of the pixel in the neighborhood window. Where each element in GLCIL is a pair of pixel index and it's frequency, g(i,j) is the frequency value of the pair having index is i,j.

Energy
Entropy
Correlation
Inertia (contrast)
Cluster Shade
Cluster Prominence
Haralick's Correlation

"advanced":
computes the following 10 texture features: Mean, Variance, Dissimilarity, Sum Average, Sum Variance, Sum Entropy, Difference of Entropies, Difference of Variances, IC1 and IC2. They are provided in this exact order in the output image. The textures are computed over a sliding window with user defined radius. To improve the speed of computation, a variant of Grey Level Co-occurrence Matrix(GLCM) called Grey Level Co-occurrence Indexed List (GLCIL) is used. Given below is the mathematical explanation on the computation of each textures. Here g( i,j) is the frequency of element in the GLCIL whose index is i,j. GLCIL stores a pair of frequency of two pixels from the given offset and the cell index ( i,j) of the pixel in the neighborhood window. (where each element in GLCIL is a pair of pixel index and it's frequency, g( i,j) is the frequency value of the pair having index is i,j.

Mean
Sum of squares: Variance
Dissimilarity
Sum average
Sum Variance
Sum Entropy
Difference variance
Difference entropy
Information Measures of Correlation IC1
Information Measures of Correlation IC2

"higher":

computes 11 local higher order statistics textures coefficients based on the grey level run-length matrix. It computes the following Haralick textures over a sliding window with user defined radius: (where p( i,j) is the element in cell i,j of a normalized Run Length Matrix (n_r) is the total number of runs and n_p is the total number of pixels ):

Short Run Emphasis
Long Run Emphasis
Grey-Level Nonuniformity
Run Length Nonuniformity
Low Grey-Level Run Emphasis
High Grey-Level Run Emphasis
Short Run Low Grey-Level Emphasis
Short Run High Grey-Level Emphasis
Long Run Low Grey-Level Emphasis
Long Run High Grey-Level Emphasis

Value

A list of RasterStacks containing the texture parameters for each combination of channel and filter

Note

The following Haralick textures are largely comparable to the results as derived by the glcm package. Find more information about the these common texture indices at the tutorial site of Mryka Hall-Beyer
Keep further in mind that this texture features are highly correlated:
Homogeneity with Contrast, r = -0.80
Homogeneity with Dissimilarity, r = -0.95
GLCM Variance with Contrast, r= 0.89
GLCM Variance with Dissimilarity, r= 0.91
GLCM Variance with Homogeneity, r= -0.83
Entropy with ASM, r= -0.87
GLCM Mean and Correlation are more independent. For the same image:
GLCM Mean shows r< 0.1 with any of the other texture measures.
GLCM Correlation shows r<0.5 with any other measure.

Author(s)

Chris Reudenbach, Thomas Nauss

References

Haralick, R.M., K. Shanmugam and I. Dinstein. 1973. Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics. SMC-3(6):610-620.
Orfeo Toolbox Sofware Guide, 2016

Examples

## Not run: 
# get some typical authority generated data
url<-"http://www.ldbv.bayern.de/file/zip/5619/DOP%2040_CIR.zip"
res <- curl::curl_download(url, "testdata.zip")
unzip(res,files = grep(".tif", unzip(res,list = TRUE)$Name,value = TRUE),junkpaths = TRUE,overwrite = TRUE)

# first initialisation of the OTB environment
initOTB()

# calculate all Haralick-textures
otbTexturesHaralick(x=file.path(getwd(),basename(grep(".tif", unzip(res,list = TRUE)$Name,value = TRUE))))

## End(Not run)

environmentalinformatics-marburg/satelliteTools documentation built on May 16, 2019, 8:16 a.m.