Összesen 1 találat.


001-es BibID:BIBFORM095974
035-os BibID:(cikkazonosító)2980 (WOS)000682299100001 (Scopus)85112129721
Első szerző:Phinzi, Kwanele
Cím:Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems / Kwanele Phinzi, Dávid Abriha, Szilárd Szabó
Megjegyzések:The availability of aerial and satellite imageries has greatly reduced the costs and time associated with gully mapping, especially in remote locations. Regardless, accurate identification of gullies from satellite images remains an open issue despite the amount of literature addressing this problem. The main objective of this work was to investigate the performance of support vector machines (SVM) and random forest (RF) algorithms in extracting gullies based on two resampling methods: bootstrapping and k-fold cross-validation (CV). In order to achieve this objective, we used PlanetScope data, acquired during the wet and dry seasons. Using the Normalized Difference Vegetation Index (NDVI) and multispectral bands, we also explored the potential of the PlanetScope image in discriminating gullies from the surrounding land cover. Results revealed that gullies had significantly different (p < 0.001) spectral profiles from any other land cover class regarding all bands of the PlanetScope image, both in the wet and dry seasons. However, NDVI was not efficient in gully discrimination. Based on the overall accuracies, RF's performance was better with CV, particularly in the dry season, where its performance was up to 4% better than the SVM's. Nevertheless, class level metrics (omission error: 11.8%; commission error: 19%) showed that SVM combined with CV was more successful in gully extraction in the wet season. On the contrary, RF combined with bootstrapping had relatively low omission (16.4%) and commission errors (10.4%), making it the most efficient algorithm in the dry season. The estimated gully area was 88 ? 14.4 ha in the dry season and 57.2 - 18.8 ha in the wet season. Based on the standard error (8.2 ha), the wet season was more appropriate in gully identification than the dry season, which had a slightly higher standard error (8.6 ha). For the first time, this study sheds light on the influence of these resampling techniques on the accuracy of satellite-based gully mapping. More importantly, this study provides the basis for further investigations into the accuracy of such resampling techniques, especially when using different satellite images other than the PlanetScope data.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
satellite imagery
gully mapping
machine learning
random forest
support vector machine
south africa
semi-arid environment
Megjelenés:Remote Sensing. - 13 : 15 (2021), p. 1-18. -
További szerzők:Abriha Dávid (1995-) (geográfus) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:TKP2020-NKA-04
Department of Higher Education and Training (DHET) of South Africa
Internet cím:Szerző által megadott URL
Intézményi repozitóriumban (DEA) tárolt változat
Rekordok letöltése1