Skip to contents

segmetric

Source: R/segmetric-package.R
segmetric-package.Rd

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.

References

  • Carleer, A.P., Debeir, O., Wolff, E., 2005. Assessment of very high spatial resolution satellite image segmentations. Photogramm. Eng. Remote. Sens. 71, 1285-1294. doi:10.14358/PERS.71.11.1285 .

  • 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 .

  • Dice, L.R., 1945. Measures of the amount of ecologic association between species. Ecology, 26(3), pp.297-302.

  • 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, Ghent.

  • Jaccard, P., 1912. The distribution of the flora in the alpine zone.

  1. New phytologist, 11(2), pp.37-50. doi:10.1111/j.1469-8137.1912.tb05611.x

  • Janssen, L.L.F., Molenaar, M., 1995. Terrain objects, their dynamics and their monitoring by the integration of GIS and remote sensing. IEEE Trans. Geosci. Remote Sens. 33, pp. 749-758. doi:10.1109/36.387590 .

  • 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. Academic Press, New York, pp. 149-160.

  • Lucieer, A., Stein, A., 2002. Existential uncertainty of spatial objects segmented from satellite sensor imagery. Geosci. Remote. Sens. IEEE Trans. 40, pp. 2518-2521. doi:10.1109/TGRS.2002.805072 .

  • Möller, 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. doi: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. doi:10.1109/TGRS.2009.2029570 .

  • Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.,

  1. 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 .

  • 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. doi: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. doi:10.1016/j.isprsjprs.2015.01.009 .

See also

Useful links:

Author

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

Authors: