Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping

	Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping
	Ding, Hu; Liu, Kai; Chen, Xiaozheng; Xiong, Liyang; Tang, Guoan; Qiu, Fang; Strobl, Josef
刊名	REMOTE SENSING
	2020
卷号	12 期号:5
英文摘要	The Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for gully erosion monitoring in this region. Among the different gully types in the Loess Plateau, the bank gully is usually regarded as the most important source for the generation of sediment. However, approaches for bank gully extraction are still limited. This study put forward an integrated framework, including segmentation optimization, evaluation and Extreme Gradient Boosting (XGBoost)-based classification, for the bank gully mapping of Zhifanggou catchment in the Chinese Loess Plateau. The approach was conducted using a 1-m resolution digital elevation model (DEM), based on unmanned aerial vehicle (UAV) photogrammetry and WorldView-3 imagery. The methodology first divided the study area into different watersheds. Then, segmentation by weighted aggregation (SWA) was implemented to generate multi-level segments. For achieving an optimum segmentation, area-weighted variance (WV) and Moran's I (MI) were adopted and calculated within each sub-watershed. After that, a new discrepancy metric, the area-number index (ANI), was developed for evaluating the segmentation results, and the results were compared with the multi-resolution segmentation (MRS) algorithm. Finally, bank gully mappings were obtained based on the XGBoost model after fine-tuning. The experiment results demonstrate that the proposed method can achieve superior segmentation compared to MRS. Moreover, the overall accuracy of the bank gully extraction results was 78.57%. The proposed approach provides a credible tool for mapping bank gullies, which could be useful for the catchment-scale gully erosion process.
内容类型	期刊论文
源URL	[http://159.226.73.51/handle/332005/20135]
专题	中国科学院南京地理与湖泊研究所
推荐引用方式 GB/T 7714	Ding, Hu,Liu, Kai,Chen, Xiaozheng,et al. Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping[J]. REMOTE SENSING,2020,12(5).
APA	Ding, Hu.,Liu, Kai.,Chen, Xiaozheng.,Xiong, Liyang.,Tang, Guoan.,...&Strobl, Josef.(2020).Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping.REMOTE SENSING,12(5).
MLA	Ding, Hu,et al."Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping".REMOTE SENSING 12.5(2020).