segm_mprofiles {OTBsegm} | R Documentation |
Morphological profiles segmentation
Description
Applies the morphological profiles segmentation algorithm to an image file or a SpatRaster
Usage
segm_mprofiles(
image,
otb,
size = 5L,
start = 1L,
step = 1L,
sigma = 1,
mode = "vector",
vector_neighbor = FALSE,
vector_stitch = TRUE,
vector_minsize = 1L,
vector_simplify = 0.1,
vector_tilesize = 1024L,
mask = NULL
)
Arguments
image |
path or |
otb |
output of |
size |
integer. Size of the profiles |
start |
integer. Initial radius of the structuring element in pixels |
step |
integer. Radius step in pixels along the profile |
sigma |
profiles values under the threshold will be ignored |
mode |
processing mode, either 'vector' or 'raster'. See details |
vector_neighbor |
logical. If FALSE (the default) a 4-neighborhood connectivity is activated. If TRUE, a 8-neighborhood connectivity is used |
vector_stitch |
logical. If TRUE (the default), scans polygons on each side of tiles and stitch polygons which connect by more than one pixel |
vector_minsize |
integer. Objects whose size in pixels is below the minimum object size will be ignored during vectorization |
vector_simplify |
simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. |
vector_tilesize |
integer. User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if NULL |
mask |
an optional raster used for masking the segmentation. Only pixels whose mask is strictly positive will be segmented |
Details
The morphological profiles segmentation algorithm is a region-based image segmentation technique that applies a series of morphological operations using structuring elements of increasing size to capture spatial patterns and textures within the image. Steps:
Morphological Filtering: The algorithm applies a sequence of openings (removing small bright structures) and closings (removing small dark structures) to the input image using structuring elements (e.g., disks, rectangles).
Profile Generation: It generates a profile for each pixel by recording the response of the morphological operations at different scales.
Feature Extraction: These profiles help capture both fine and coarse structures within the image, creating a set of features that can be used for classification or segmentation.
Segmentation (Optional): The extracted profiles can be input into a classifier or segmentation algorithm to differentiate between regions with distinct spatial characteristics.
The processing mode 'vector' will output a vector file, and process the input image piecewise. This allows performing segmentation of very large images. IN contrast, 'raster' mode will output a labeled raster, and it cannot handle large data. If mode is 'raster', all the 'vector_*' arguments are ignored.
Value
sf
or SpatRaster
Examples
## Not run:
## load packages
library(link2GI)
library(OTBsegm)
library(terra)
## load sample image
image_sr <- rast(system.file("raster/pnoa.tiff", package = "OTBsegm"))
## connect to OTB (change to your directory)
otblink <- link2GI::linkOTB(searchLocation = "C:/OTB/")
## apply segmentation
results_ms_sf <- segm_mprofiles(
image = image_sr,
otb = otblink,
size = 5,
start = 3,
step = 20,
sigma = 1
)
## End(Not run)