Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1007/s11356-023-25861-5
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Title (Primary) Superpixel segmentation integrated feature subset selection for wetland classification over Yellow River Delta
Author Cui, L.; Zhang, J.; Wu, Z.; Xun, L.; Wang, X.; Zhang, S.; Bai, Y.; Zhang, S.; Yang, S.; Liu, Q.
Source Titel Environmental Science and Pollution Research
Year 2023
Department RS
Volume 30
Issue 17
Page From 50796
Page To 50814
Language 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.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=23581
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