sits_texture {sits} | R Documentation |
Apply a set of texture measures on a data cube.
Description
A set of texture measures based on the Grey Level Co-occurrence Matrix (GLCM) described by Haralick. Our implementation follows the guidelines and equations described by Hall-Beyer (both are referenced below).
Usage
sits_texture(cube, ...)
## S3 method for class 'raster_cube'
sits_texture(
cube,
...,
window_size = 3L,
angles = 0,
memsize = 4L,
multicores = 2L,
output_dir,
progress = TRUE
)
## S3 method for class 'derived_cube'
sits_texture(cube, ...)
## Default S3 method:
sits_texture(cube, ...)
Arguments
cube |
Valid sits cube |
... |
GLCM function (see details). |
window_size |
An odd number representing the size of the sliding window. |
angles |
The direction angles in radians related to the central pixel and its neighbor (See details). Default is 0. |
memsize |
Memory available for classification (in GB). |
multicores |
Number of cores to be used for classification. |
output_dir |
Directory where files will be saved. |
progress |
Show progress bar? |
Details
The spatial relation between the central pixel and its neighbor is expressed in radians values, where: #'
0
: corresponds to the neighbor on right-sidepi/4
: corresponds to the neighbor on the top-right diagonalspi/2
: corresponds to the neighbor on above3*pi/4
: corresponds to the neighbor on the top-left diagonals
Our implementation relies on a symmetric co-occurrence matrix, which
considers the opposite directions of an angle. For example, the neighbor
pixels based on 0
angle rely on the left and right direction; the
neighbor pixels of pi/2
are above and below the central pixel, and
so on. If more than one angle is provided, we compute their average.
Value
A sits cube with new bands, produced according to the requested measure.
Available texture functions
glcm_contrast()
: measures the contrast or the amount of local variations present in an image. Low contrast values indicate regions with low spatial frequency.glcm_homogeneity()
: also known as the Inverse Difference Moment, it measures image homogeneity by assuming larger values for smaller gray tone differences in pair elements.glcm_asm()
: the Angular Second Moment (ASM) measures textural uniformity. High ASM values indicate a constant or a periodic form in the window values.glcm_energy()
: measures textural uniformity. Energy is defined as the square root of the ASM.glcm_mean()
: measures the mean of the probability of co-occurrence of specific pixel values within the neighborhood.glcm_variance()
: measures the heterogeneity and is strongly correlated to first order statistical variables such as standard deviation. Variance values increase as the gray-level values deviate from their mean.glcm_std()
: measures the heterogeneity and is strongly correlated to first order statistical variables such as standard deviation. STD is defined as the square root of the variance.glcm_correlation()
: measures the gray-tone linear dependencies of the image. Low correlation values indicate homogeneous region edges.
Author(s)
Felipe Carvalho, felipe.carvalho@inpe.br
Felipe Carlos, efelipecarlos@gmail.com
Rolf Simoes, rolf.simoes@inpe.br
Gilberto Camara, gilberto.camara@inpe.br
References
Robert M. Haralick, K. Shanmugam, Its'Hak Dinstein, "Textural Features for Image Classification", IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 6, 610-621, 1973, DOI: 10.1109/TSMC.1973.4309314.
Hall-Beyer, M., "GLCM Texture Tutorial", 2007, http://www.fp.ucalgary.ca/mhallbey/tutorial.htm.
Hall-Beyer, M., "Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales", International Journal of Remote Sensing, 38, 1312–1338, 2017, DOI: 10.1080/01431161.2016.1278314.
A. Baraldi and F. Panniggiani, "An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters," IEEE Transactions on Geoscience and Remote Sensing, 33, 2, 293-304, 1995, DOI: 10.1109/TGRS.1995.8746010.
Shokr, M. E., "Evaluation of second-order texture parameters for sea ice classification from radar images", J. Geophys. Res., 96, 10625–10640, 1991, DOI:10.1029/91JC00693.
Peng Gong, Danielle J. Marceau, Philip J. Howarth, "A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data", Remote Sensing of Environment, 40, 2, 1992, 137-151, DOI: 10.1016/0034-4257(92)90011-8.
Examples
if (sits_run_examples()) {
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
cube <- sits_cube(
source = "BDC",
collection = "MOD13Q1-6.1",
data_dir = data_dir
)
# Compute the NDVI variance
cube_texture <- sits_texture(
cube = cube,
NDVIVAR = glcm_variance(NDVI),
window_size = 5,
output_dir = tempdir()
)
}