The segmetric is an open source package that provides a
set of metrics for analyzing and evaluating geospatial segmentations. It
implements 28 supervised metrics used in literature for spatial
segmentation assessment (see References below).
Installation
# install via CRAN install.packages("segmetric")
Development version
To install the development version of segmetric, run the
following commands:
This research was supported by the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation program
(Grant agreement No 677140 MIDLAND).
References
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spatial resolution satellite image segmentations. Photogramm. Eng.
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Clinton, N., Holt, A., Scarborough, J., Yan, L., Gong, P., 2010.
Accuracy assessment measures for object-based image segmentation
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Feitosa, R.Q., Ferreira, R.S., Almeida, C.M., Camargo, F.F., Costa,
G.A.O.P., 2010. Similarity metrics for genetic adaptation of
segmentation parameters. In: 3rd International Conference on Geographic
Object-Based Image Analysis (GEOBIA 2010). The International Archives of
the Photogrammetry, Remote Sensing and Spatial Information Sciences,
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Levine, M.D., Nazif, A.M., 1982. An experimental rule based system
for testing low level segmentation strategies. In: Preston, K., Uhr, L.
(Eds.), Multicomputers and Image Processing: Algorithms and Programs.
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Lucieer, A., Stein, A., 2002. Existential uncertainty of spatial
objects segmented from satellite sensor imagery. Geosci. Remote. Sens.
IEEE Trans. 40, pp. 2518-2521. http://dx.doi.org/10.1109/TGRS.2002.805072.
Moller, M., Lymburner, L., Volk, M., 2007. The comparison index: a
tool for assessing the accuracy of image segmentation. Int. J. Appl.
Earth Obs. Geoinf. 9, pp. 311-321. http://dx.doi.org/10.1016/j.jag.2006.10.002.
Persello, C., Bruzzone, L., 2010. A novel protocol for accuracy
assessment in classification of very high resolution images. IEEE Trans.
Geosci. Remote Sens. 48, pp. 1232-1244. http://dx.doi.org/10.1109/TGRS.2009.2029570.
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I.,
Savarese, S.,
In: Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition (CVPR), pp. 658-666.
Van Coillie, F.M.B., Verbeke, L.P.C., De Wulf, R.R., 2008.
Semi-automated forest stand delineation using wavelet based segmentation
of very high resolution optical imagery. In: Object-Based Image
Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing
Applications, pp. 237-256. http://dx.doi.org/10.1007/978-3-540-77058-9_13.
Van Rijsbergen, C.J., 1979. Information Retrieval.
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Weidner, U., 2008. Contribution to the assessment of segmentation
quality for remote sensing applications. Int. Arch. Photogramm. Remote
Sens. Spat. Inf. Sci. 37, pp. 479-484.
Yang, J., Li, P., He, Y., 2014. A multi-band approach to
unsupervised scale parameter selection for multi-scale image
segmentation. ISPRS J. Photogramm. Remote Sens. 94, pp. 13-24. http://dx.doi.org/10.1016/j.isprsjprs.2014.04.008.
Yang, J., He, Y., Caspersen, J. P., Jones, T. A., 2017. Delineating
Individual Tree Crowns in an Uneven-Aged, Mixed Broadleaf Forest Using
Multispectral Watershed Segmentation and Multiscale Fitting. IEEE J.
Sel. Top. Appl. Earth Obs. Remote Sens., 10(4), pp. 1390-1401. http://dx.doi.org/10.1109/JSTARS.2016.2638822.
Zhan, Q., Molenaar, M., Tempfli, K., Shi, W., 2005. Quality
assessment for geo‐spatial objects derived from remotely sensed data.
International Journal of Remote Sensing, 26(14), pp.2953-2974. http://dx.doi.org/10.1080/01431160500057764.
Zhang, X., Feng, X., Xiao, P., He, G., Zhu, L., 2015a. Segmentation
quality evaluation using region-based precision and recall measures for
remote sensing images. ISPRS J. Photogramm. Remote Sens. 102, pp. 73-84.
http://dx.doi.org/10.1016/j.isprsjprs.2015.01.009.
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