1. Recognizing landslides in remote sensing images based on enhancement of information in digital elevation models.
- Author
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Jia, Lu, Leng, Xiaopeng, Wang, Xingchen, and Nie, Manyuan
- Subjects
DIGITAL elevation models ,LANDSLIDES ,TRANSFORMER models ,IMAGE intensifiers ,IMAGE recognition (Computer vision) ,REMOTE sensing ,REMOTE-sensing images - Abstract
To address the landslide recognition problem in remote sensing images, this paper designs a visual transformer network model based on DEM (digital elevation model) feature enhancement, which is experimentally validated on the Bijie landslide dataset and Landslide4Sense2022 dataset. The lion optimizer is used during training. The results show that 98.49% accuracy and 97.24% F1 score are achieved on Bijie dataset, and 88.22% accuracy and 90.16% F1 score on Landslide4Sense2022 dataset, which is a significant improvement in landslide recognition compared with other mainstream network models. Therefore, it can be found that this paper's method is effective in the recognition of landslide from remote sensing images. Firstly, the swin transformer network model was successfully applied to remote sensing landslide image classification by means of transfer learning. Secondly, an information enhancement approach based on DEM features was designed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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