| Title: | Agriculture Image Feature |
|---|---|
| Description: | Functions to calculate Gray Level Co-occurrence Matrix(GLCM), RGB-based Vegetative Index(RGB VI) and Normalized Difference Vegetation Index(NDVI) family image features. GLCM calculations are based on Haralick (1973) <doi:10.1109/TSMC.1973.4309314>. |
| Authors: | Chun-Han Lee [aut] <[email protected]> Li-Yu Daisy Liu [aut] <[email protected]> |
| Maintainer: | Chun-Han Lee <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.3 |
| Built: | 2024-11-27 06:46:04 UTC |
| Source: | CRAN |
This function calculate Gray Level Co-occurrence Matrix(GLCM), which can be used to describe texture of the image. The default parameters is distance = 1, angle = 0, gray level = 8
GLCM(x, d = 1, angle = 0, ngray = 8, grayscale = c(0,255), symmetric = TRUE, prob = FALSE)GLCM(x, d = 1, angle = 0, ngray = 8, grayscale = c(0,255), symmetric = TRUE, prob = FALSE)
x |
A numeric matrix. |
d |
an integer value, the distance between the current pixel, and the pixel to which it is compared. |
angle |
one of "0", "45", "90" or "135", the pixel to which the current pixel is compared. |
ngray |
an integer value, the number of gray levels to use in calculate GLCM. |
grayscale |
a vector which contain c(min,max) to set the range of value , if NULL grayscale will be set into the min and max value in x. |
symmetric |
Logical value, if TRUE (default) the matrix will be turn into a symmetric GLCM. |
prob |
Logical value, if TRUE (default) the matrix will be normalized such that the sum of it's components is 1. |
A GLCM with dimension ngray*ngray table. Each column and row represent a gray level in the image matrix.
Hall-Beyer, M. (2000). GLCM texture: a tutorial. National Council on Geographic Information and Analysis Remote Sensing Core Curriculum, 3, 75.
# generate an image data matrix with range 0~255 set.seed(100) m <- matrix(sample(0:255,64), nrow=8, ncol=8) # calculate GLCM with defalut parameters GLCM.m <- GLCM(m) # calculate probability GLCM GLCM.m.p <- GLCM(m,prob = TRUE)# generate an image data matrix with range 0~255 set.seed(100) m <- matrix(sample(0:255,64), nrow=8, ncol=8) # calculate GLCM with defalut parameters GLCM.m <- GLCM(m) # calculate probability GLCM GLCM.m.p <- GLCM(m,prob = TRUE)
This function calculate NDVI value from input near-infrared (NIR) and red bands images. If change the input from red band into green or red-edge band values, it will return GNDVI or NDRE values.
NDVI(NIR, R)NDVI(NIR, R)
NIR |
a dataframe or matrix contains NIR band values. |
R |
a dataframe or matrix contains red band values, NIR and R should have same class and dimension.. |
A matrix or dataframe(depends on the class of NIR and R).
De Swaef, T., Maes, W. H., Aper, J., Baert, J., Cougnon, M., Reheul, D., ... & Lootens, P. (2021). Applying RGB-and thermal-based vegetation indices from UAVs for high-throughput field phenotyping of drought tolerance in forage grasses. Remote Sensing, 13(1), 147.
This function calculate some useful RGB based vegetative indexes.
RGBVI(R, G, B, vi = c('RCC','GCC','ExG2','ExR','ExGR' ,'GRVI','VDVI','VARI','MGRVI','CIVE','VEG'))RGBVI(R, G, B, vi = c('RCC','GCC','ExG2','ExR','ExGR' ,'GRVI','VDVI','VARI','MGRVI','CIVE','VEG'))
R |
a dataframe or matrix contains 'red' values, RGB should have same class and dimension.. |
G |
a dataframe or matrix contains 'green' values, RGB should have same class and dimension. |
B |
a dataframe or matrix contains 'blue' values, RGB should have same class and dimension.. |
vi |
vegetative indexes to be calculated(see Details). |
The vi parameter should be a characteristic vector and include
at least one of the following VI: 'RCC','GCC','ExG2','ExR','ExGR','GRVI','VDVI','VARI','MGRVI','CIVE','VEG'.
By default, all of the VIs will be calculated.
A list with length(vi). Each elements represent a vegetative index matrix or data frame.
De Swaef, T., Maes, W. H., Aper, J., Baert, J., Cougnon, M., Reheul, D., ... & Lootens, P. (2021). Applying RGB-and thermal-based vegetation indices from UAVs for high-throughput field phenotyping of drought tolerance in forage grasses. Remote Sensing, 13(1), 147.
# generate R, G, B matrix with range 0~255 R <- matrix(sample(0:255,25), nrow=5, ncol=5) G <- matrix(sample(0:255,25), nrow=5, ncol=5) B <- matrix(sample(0:255,25), nrow=5, ncol=5) # calculate all available rgb vi vi.all <- RGBVI(R,G,B) # calculate RCC,GCC,ExGR,MGRVI vi.4 <- RGBVI(R,G,B,vi=c('RCC','GCC','ExGR','MGRVI'))# generate R, G, B matrix with range 0~255 R <- matrix(sample(0:255,25), nrow=5, ncol=5) G <- matrix(sample(0:255,25), nrow=5, ncol=5) B <- matrix(sample(0:255,25), nrow=5, ncol=5) # calculate all available rgb vi vi.all <- RGBVI(R,G,B) # calculate RCC,GCC,ExGR,MGRVI vi.4 <- RGBVI(R,G,B,vi=c('RCC','GCC','ExGR','MGRVI'))