These functions compute subsets required to calculate segmentation metrics as described in Clinton et al. (2010) and Costa et al. (2017).
sm_ref()
returns the set of \(n\) polygons of reference, represented by \(X = \{x_{i}: i = 1, ....., n\}\)sm_seg()
returns the set of \(m\) segmentation polygons, represented by \(Y = \{y_{j}: j = 1, ....., m\}\)sm_ytilde()
returns \(\tilde{Y}_{i}\), a subset of \(Y\), where \(\tilde{Y}_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) \neq 0\}\)sm_xtilde()
returns \(\tilde{X}_{j}\), a subset of \(X\), where \(\tilde{X}_{j} = \{x_{i}: \rm{area}(y_{j} \cap x_{i}) \neq 0\}\)sm_yprime()
returns \(Y'_{i}\), a subset of \(Y\), where \(Y'_{i} = \{y_{j}: max(\rm{area}(x_{i} \cap y_{j}))\}\)sm_xprime()
returns \(X'_{j}\), a subset of \(X\), where \(X'_{j} = \{x_{i}: max(\rm{area}(y_{j} \cap x_{i}))\}\)sm_ya()
returns \(Y\!a_{i}\), a subset of \(\tilde{Y}_{i}\), where \(Y\!a_{i} = \{y_{j}: \rm{centroid}(x_{i}) \:\rm{in}\: y_{j}\}\)sm_yb()
returns \(Y\!b_{i}\), a subset of \(\tilde{Y}_{i}\), where \(Y\!b_{i} = \{y_{j}: \rm{centroid}(y_{j}) \:\rm{in}\: x_{i}\}\)sm_yc()
returns \(Y\!c_{i}\), a subset of \(\tilde{Y}_{i}\), where \(Y\!c_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) / \rm{area}(y_{j}) > 0.5\}\)sm_yd()
returns \(Y\!d_{i}\), a subset of \(\tilde{Y}_{i}\), where \(Y\!d_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) / \rm{area}(x_{i}) > 0.5\}\)sm_ystar()
returns \({Y}^{*}_{i}\), where \({Y}^{*}_{i} = Y\!a_{i} \cup Y\!b_{i} \cup Y\!c_{i} \cup Y\!c_{i}\)sm_ycd()
returns \(Y\!cd_{i}\), where \(Y\!cd_{i} = Y\!c_{i} \cup Y\!d_{i}\)sm_ye()
returns \(Y\!e_{i}\), a subset of \(\tilde{Y}_{i}\), where \(Y\!e_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) / \rm{area}(y_{j}) = 1\}\)sm_yf()
returns \(Y\!f_{i}\), a subset of \(\tilde{Y}_{i}\), where \(Y\!f_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) / \rm{area}(y_{j}) > 0.55\}\)sm_yg()
returns \(Y\!g_{i}\), a subset of \(\tilde{Y}_{i}\), where \(Y\!g_{i} = \{y_{j}: \rm{area}(x_{i} \cap y_{j}) / \rm{area}(y_{j}) > 0.75\}\)
Usage
sm_ytilde(m)
sm_xtilde(m)
sm_yprime(m)
sm_xprime(m)
sm_ya(m)
sm_yb(m)
sm_yc(m)
sm_yd(m)
sm_ystar(m)
sm_ycd(m)
sm_ye(m)
sm_yf(m)
sm_yg(m)
Value
sm_ref()
: Return an object of classref_sf
(inherited fromsf
) containing identification (ref_id
) and geometry (geometry
) columns.sm_seg()
: Return an object of classseg_sf
(inherited fromsf
) containing identification (seg_id
) and geometry (geometry
) columns.sm_ytilde()
,sm_xtilde()
,sm_yprime()
,sm_xprime()
,sm_ya()
,sm_yb()
,sm_yc()
,sm_yd()
,sm_ystar()
,sm_ycd()
,sm_ye()
,sm_yf()
, andsm_yg()
: Return an object of classsubset_sf
(inherited fromsf
) containing identification (ref_id
andseg_id
), and geometry (geometry
) columns.
References
Clinton, N., Holt, A., Scarborough, J., Yan, L., & Gong, P. (2010). Accuracy Assessment Measures for Object-based Image Segmentation Goodness. Photogrammetric Engineering & Remote Sensing, 76(3), 289–299. doi:10.14358/PERS.76.3.289 .
Costa, H., Foody, G. M., & Boyd, D. S. (2018). Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sensing of Environment, 205(December 2017), 338–351. doi:10.1016/j.rse.2017.11.024 .