R: segmetric

segmetric-package {segmetric}

R Documentation

segmetric

Description

Metrics for assessing segmentation accuracy for geospatial data.

Purpose

The segmetric package provides a set of metrics for the segmentation accuracy assessment (or evaluation) of geospatial data. It includes more than 20 metrics used in the literature for spatial segmentation assessment (Van Rijsbergen, 1979; Levine and Nazif, 1982; Janssen and Molenaar, 1995; Lucieer and Stein, 2002; Carleer et al., 2005; Moller et al., 2007; van Coillie et al., 2008; Costa et al., 2008; Weidner, 2008; Feitosa et al., 2010; Clinton et al. 2010; Persello and Bruzzone, 2010; Yang et al., 2014; and Zhang et al., 2015).

Extensions

The segmetric package is extensible and provides a set of functions to ease the implementation of new metrics. See ?sm_reg_metric() to find how new metrics are implemented.

Contributions

Contribution to this package could be done at segmetric's page on GitHub: https://github.com/michellepicoli/segmetric.

Author(s)

Maintainer: Michelle Picoli mipicoli@gmail.com (ORCID)

Authors:

Rolf Simoes rolfsimoes@gmail.com (ORCID)
Alber Sanchez albhasan@gmail.com (ORCID)

References

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Clinton, N., Holt, A., Scarborough, J., Yan, L., Gong, P., 2010. Accuracy assessment measures for object-based image segmentation goodness. Photogramm. Eng. Remote. Sens. 76, pp. 289-299.
Costa, G.A.O.P., Feitosa, R.Q., Cazes, T.B., Feijo, B., 2008. Genetic adaptation of segmentation parameters. In: Blaschke, T., Lang, S., Hay, G.J. (Eds.), Object-based Image Analysis. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 679-695. doi: 10.1007/978-3-540-77058-9_37.
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New phytologist, 11(2), pp.37-50. doi: 10.1111/j.1469-8137.1912.tb05611.x

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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. doi: 10.1109/TGRS.2009.2029570.
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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. doi: 10.1007/978-3-540-77058-9_13.
Van Rijsbergen, C.J., 1979. Information Retrieval. Butterworth-Heinemann, London.
Weidner, U., 2008. Contribution to the assessment of segmentation quality for remote sensing applications. In: Proceedings of the 21st Congress for the International Society for Photogrammetry and Remote Sensing, 03–11 July, Beijing, China. Vol. XXXVII. Part B7, 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. doi: 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. doi: 10.1109/JSTARS.2016.2638822.
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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. doi: 10.1016/j.isprsjprs.2015.01.009.