Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1007/s11356-023-25861-5
Volltext Shareable Link
Titel (primär) Superpixel segmentation integrated feature subset selection for wetland classification over Yellow River Delta
Autor Cui, L.; Zhang, J.; Wu, Z.; Xun, L.; Wang, X.; Zhang, S.; Bai, Y.; Zhang, S.; Yang, S.; Liu, Q.
Quelle Environmental Science and Pollution Research
Erscheinungsjahr 2023
Department RS
Band/Volume 30
Heft 17
Seite von 50796
Seite bis 50814
Sprache englisch
Topic T5 Future Landscapes
Keywords Wetland classification; Superpixel segmentation; Random forest; Feature selection; Machine learning
Abstract Wetlands are one of the world’s most significant and vulnerable ecosystems. The wetlands of the Yellow River Delta are subject to multiple influences of ocean tidal action and the massive sediment deposits of the Yellow River, resulting in a more complex and unstable composition of land cover types. To better distinguish the wetlands in the region, we conducted the classification using an object-oriented combined with feature preference machine learning approach. To alleviate the pretzel phenomenon in pixel-based classification, a superpixel segmentation method using the watershed algorithm with H-minima labeling was used to segment the images at the optimal scale. The best feature subset for classification was filtered using the recursive feature elimination cross-validation approach, which extracts multiple spectral indices from the images. A random forest classifier combining superpixel segmentation and feature selection methods was proposed for the wetland classification. The model improves the classification accuracy of wetlands compared to three classical pixel-based machine learning classification methods. And the overall accuracy was 91.74% and the kappa coefficient was 0.9078, both of which were improved by about 4.53% and 0.0506, respectively, compared with the best-performing random forest classifier in pixel-oriented. The results showed that this method can effectively improve the classification accuracy of the Yellow River Delta wetlands compared with the previous studies.
dauerhafte UFZ-Verlinkung
Cui, L., Zhang, J., Wu, Z., Xun, L., Wang, X., Zhang, S., Bai, Y., Zhang, S., Yang, S., Liu, Q. (2023):
Superpixel segmentation integrated feature subset selection for wetland classification over Yellow River Delta
Environ. Sci. Pollut. Res. 30 (17), 50796 - 50814 10.1007/s11356-023-25861-5