metric_functions | R Documentation |
The sm_compute()
computes a given metric (metric_id
parameter) from
segmentation objects. It compares the reference to the segmentation
polygons using a metric.
A list with all supported metrics can be obtained
by sm_list_metrics()
(see Details for more information).
The sm_metric_subset()
returns the subset used to compute the metrics
in segmetric object.
sm_compute(m, metric_id, ...) sm_metric_subset(m, metric_id = NULL)
m |
A |
metric_id |
A |
... |
Any additional argument to compute a metric (see Details). |
"OS1
" refers to Oversegmentation. Its values range from 0 (optimal) to 1
(Clinton et al., 2010).
"US1
" refers to Undersegmentation. Its values range from 0 (optimal) to 1
(Clinton et al., 2010).
"OS2
" refers to Oversegmentation. Its values range from 0 (optimal) to 1
(Persello and Bruzzone, 2010).
"US2
" refers to Undersegmentation. Its values range from 0 (optimal) to 1
(Persello and Bruzzone, 2010).
"OS3
" refers to Oversegmentation. Its values range from 0 (optimal) to 1
(Yang et al., 2014).
"US3
" refers to Undersegmentation. Its values range from 0 (optimal) to 1
(Yang et al., 2014).
"AFI
" refers to Area Fit Index. Its optimal value is 0 (Lucieer and Stein,
2002; Clinton et al., 2010).
"QR
" refers to Quality Rate. Its values range from 0 (optimal) to 1
(Weidner, 2008; Clinton et al., 2010).
"D_index
" refers to Index D. Its values range from 0 (optimal) to 1
(Levine and Nazif, 1982; Clinton et al., 2010).
"precision
" refers to Precision. Its values range from 0 to 1 (optimal)
(Van Rijsbergen, 1979; Zhang et al., 2015).
"recall
" refers to Recall. Its values range from 0 to 1 (optimal) (Van
Rijsbergen, 1979; Zhang et al., 2015).
"UMerging
" refers to Undermerging. Its values range from 0 (optimal) to 1
(Levine and Nazif, 1982; Clinton et al., 2010).
"OMerging
" refers to Overmerging. Its optimal value is 0
(Levine and Nazif, 1982; Clinton et al., 2010).
"M
" refers to Match. Its values range from 0 to 1 (optimal) (Janssen and
Molenaar, 1995; Feitosa et al., 2010).
"E
" refers to Evaluation Measure. Its values range from 0 (optimal) to 100
(Carleer et al., 2005).
"RAsub
" refers to Relative Area. Its values range from 0 to 1 (optimal)
(Müller et al., 2007; Clinton et al., 2010).
"RAsuper
" refers to Relative area. Its values range from 0 to 1 (optimal)
(Müller et al., 2007; Clinton et al., 2010).
"PI
" refers to Purity Index. Its values range from 0 to 1 (optimal) (van
Coillie et al., 2008).
"Fitness
" refers to Fitness Function. Its optimal value is 0 (Costa et al.,
2008).
"ED3
" refers to Euclidean Distance. Its values range from 0 (optimal) to 1
(Yang et al., 2014).
"F_measure
" refers to F-measure metric. Its values range from 0 to 1
(optimal) (Van Rijsbergen, 1979; Zhang et al., 2015). It takes the optional
weight argument alpha
, ranging from 0.0
to 1.0
(the default is 0.5
).
"IoU
" refers to Intersection over Union metric. Its values range
from 0 to 1 (optimal) (Jaccard, 1912; Rezatofighi et al., 2019).
"SimSize
" refers to the similarity size metric. Its values range from
0 to 1 (optimal) (Zhan et al., 2005).
"qLoc
"refers to quality of object’s location metric. Its optimal value
is 0 (Zhan et al., 2005).
"RPsub
" refers to Relative Position (sub) metric. Optimal value is 0
(Möller et al., 2007, Clinton et al., 2010).
"RPsuper
" refers to Relative Position (super) metric. Its values range
from 0 (optimal) to 1 (Möller et al., 2007, Clinton et al., 2010).
"OI2
refers to Overlap Index metric. Its values range from 0 to 1
(optimal) (Yang et al., 2017).
Return a numeric
vector with computed metric.
A complete list of cited references is available in ?segmetric
.
sm_list_metrics()
# load sample datasets data("sample_ref_sf", package = "segmetric") data("sample_seg_sf", package = "segmetric") # create segmetric object m <- sm_read(ref_sf = sample_ref_sf, seg_sf = sample_seg_sf) # compute AFI metric and summarize it sm_compute(m, "AFI") %>% summary() # compute three metrics and summarize them sm_compute(m, c("AFI", "OS1", "US2")) %>% summary() # compute OS1, F_measure, and US2 metrics using pipe m <- sm_compute(m, "OS1") %>% sm_compute("F_measure") %>% sm_compute("US2") # summarize them summary(m)
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