Name
Abstract | ||
|---|---|---|
The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional distribution and evolution process. However, the existing automated methods to characterize yardangs are of limited generalization that may only be feasible for specific types of yardangs in certain areas. Deep learning methods, which are superior in representation learning, provide potential solutions for mapping yardangs with complex and variable features. In this study, we apply Mask region-based convolutional neural networks (Mask R-CNN) to automatically delineate and classify yardangs using very high spatial resolution images from Google Earth. The yardang field in the Qaidam Basin, northwestern China is selected to conduct the experiments and the method yields mean average precisions of 0.869 and 0.671 for intersection of union (IoU) thresholds of 0.5 and 0.75, respectively. The manual validation results on images of additional study sites show an overall detection accuracy of 74%, while more than 90% of the detected yardangs can be correctly classified and delineated. We then conclude that Mask R-CNN is a robust model to characterize multi-scale yardangs of various types and allows for the research of the morphological and evolutionary aspects of aeolian landform. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13040733 | REMOTE SENSING |
Keywords | DocType | Volume |
aeolian landform, yardang, morphological characteristic, deep learning, Mask R-CNN, Google Earth imagery | Journal | 13 |
Issue | Citations | PageRank |
4 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bowen Gao | 1 | 0 | 0.34 |
| Ninghua Chen | 2 | 0 | 0.34 |
| Thomas Blaschke | 3 | 0 | 3.72 |
| Chase Q. Wu | 4 | 0 | 1.35 |
| Jianyu Chen | 5 | 15 | 5.91 |
| Yaochen Xu | 6 | 0 | 0.34 |
| Xiaoping Yang | 7 | 0 | 0.34 |
| Zhenhong Du | 8 | 31 | 16.98 |