Apply a set of texture measures on a data cube.

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?

Value

A sits cube with new bands, produced according to the requested measure.

Details

The spatial relation between the central pixel and its neighbor is expressed in radians values, where: #'

0: corresponds to the neighbor on right-side
pi/4: corresponds to the neighbor on the top-right diagonals
pi/2: corresponds to the neighbor on above
3*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.

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.

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.

Author

Felipe Carvalho, felipe.carvalho@inpe.br

Felipe Carlos, efelipecarlos@gmail.com

Rolf Simoes, rolf.simoes@inpe.br

Gilberto Camara, gilberto.camara@inpe.br

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()
    )
}