The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest
Abstract
Novel computational and statistical prediction methods such as the support vector machine are becoming increasingly popular in remote-sensing applications and need to be compared to more traditional approaches like maximum-likelihood classification. However, the accuracy assessment of such predictive models in a spatial context needs to account for the presence of spatial autocorrelation in geospatial data by using spatial cross-validation and bootstrap strategies instead of their now more widely used non-spatial equivalent. These spatial resampling-based estimation procedures were therefore implemented in a new package `sperrorest' for the open-source statistical data analysis software R. This package is introduced using the example of the detection of rock-glacier flow structures from IKONOS-derived Gabor texture features and terrain attribute data.
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Citations
Cites
Support vector machines in remote sensing: a review
Mountrakis, G., Im, J., & Ogole, C. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.11.001 · Cited by 3,473
Cited by
Valavi, R., Elith, J., Lahoz‐Monfort, J. J., & Guillera‐Arroita, G. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13107 · Cited by 1,056
Predicting into unknown space? Estimating the area of applicability of spatial prediction models
Meyer, H., & Pebesma, E. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13650 · Cited by 662
McDonald, R. I., Biswas, T., Sachar, C., Housman, I., Boucher, T. M., Balk, D., Nowak, D., Spotswood, E., Stanley, C. K., & Leyk, S. (2021) · PLOS ONE · doi:10.1371/journal.pone.0249715 · Cited by 159
Testing species assemblage predictions from stacked and joint species distribution models
Zurell, D., Zimmermann, N. E., Gross, H., Baltensweiler, A., Sattler, T., & Wüest, R. O. (2020) · Journal of Biogeography · doi:10.1111/jbi.13608 · Cited by 153
Dealing with clustered samples for assessing map accuracy by cross-validation
de Bruin, S., Brus, D. J., Heuvelink, G. B. M., van Ebbenhorst Tengbergen, T., & Wadoux, A. M. J.-C. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101665 · Cited by 122
Assessing and improving the transferability of current global spatial prediction models
Ludwig, M., Moreno‐Martinez, A., Hölzel, N., Pebesma, E., & Meyer, H. (2023) · Global Ecology and Biogeography · doi:10.1111/geb.13635 · Cited by 92
kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation
Linnenbrink, J., Milà, C., Ludwig, M., & Meyer, H. (2024) · Geoscientific Model Development · doi:10.5194/gmd-17-5897-2024 · Cited by 51
“Namely, we assessed the performance of random 10-fold CV, spatial 10-fold CV based on k -means clustering of the geographical coordinates (Brenning, 2012), and 10-fold kNNDM CV .”
Spatial machine-learning model diagnostics: a model-agnostic distance-based approach
Brenning, A. (2023) · International Journal of Geographical Information Science · doi:10.1080/13658816.2022.2131789 · Cited by 33
Self-citationBrenning, A. (2023) · Machine Learning · doi:10.1007/s10994-023-06327-8 · Cited by 26
Self-citation
Metadata
- Authors
- Brenning, A.
- Venue
- 2012 IEEE International Geoscience and Remote Sensing Symposium
- Publisher
- IEEE
- Published
- 2012-07
- Pages
- 5372-5375
- OpenAlex
- W2038546254
- Cited by
- 266
- References
- 1
- FWCI
- 10.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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