A support mask identifies the spatial elements eligible for segmentation. It does not assign region labels. Capivara can use an explicit missing-data footprint, a white-light starlet support, or an adaptive multi-band support.
Starlet support
Both segmentation backends accept the same starlet controls:
seg <- segment_large(
input = cube,
Ncomp = 25,
use_starlet_mask = TRUE,
support_method = "starlet",
starlet_J = 5,
starlet_scales = 2:5,
include_coarse = FALSE,
denoise_k = 0,
starlet_mode = "soft",
positive_only = TRUE,
mask_mode = "na",
knn_k = 100
)The cube is collapsed to a white-light image, decomposed across
starlet scales, and reconstructed from the selected scales.
include_coarse controls the coarse plane;
denoise_k is expressed in MAD units; and
positive_only excludes negative reconstructed values. These
settings determine the support.
Plot the support before segmentation:
support_result <- build_starlet_mask(
cube,
starlet_J = 5,
starlet_scales = 2:5,
include_coarse = FALSE,
denoise_k = 0,
mode = "soft",
positive_only = TRUE
)
support_result$maskAdaptive support
The adaptive builder can combine per-band evidence and optional transforms.
adaptive <- build_adaptive_support(
cube,
transform = "asinh",
sky_method = "border",
border_fraction = 0.1,
z_threshold = 3,
min_band_persistence = 2
)
seg <- segment(
cube,
Ncomp = 15,
use_starlet_mask = TRUE,
support_method = "adaptive",
support_args = list(
transform = "asinh",
sky_method = "border",
z_threshold = 3,
min_band_persistence = 2
)
)The transform, sky estimator, persistence, and thresholds determine the adaptive support. Excluded spaxels remain unassigned.