博士生导师
硕士生导师
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学历：博士研究生毕业
学位：工学博士学位
办公地点：西南交通大学犀浦校区地球科学与工程学院 X4139
毕业院校：西南交通大学
学科：测绘科学与技术
所在单位：地球科学与工程学院
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Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods
发表刊物：The Cryosphere
摘要：Remote sensing extraction of glacial lakes is an effective way of monitoring water body distribution and outburst events. At present, the lack of glacial lake datasets and the edge recognition problem of semantic segmentation networks lead to poor accuracy and inaccurate outlines of glacial lakes. Therefore, this study constructed a high-resolution dataset containing seven types of glacial lakes and proposed a refined glacial lake extraction method, which combines the LinkNet50 network for rough extraction and simple linear iterative clustering (SLIC) dense conditional random field (DenseCRF) for optimization. The results show that (1) with Google Earth images of 0.52 m resolution in the study area, the recall, precision, F1 score, and intersection over union (IoU) of glacial lake extraction based on the proposed method are 96.52 %, 92.49 %, 94.46 %, and 90.69 %, respectively, and (2) with the Google Earth images of 2.11 m resolution in the Qomolangma National Nature Reserve, 2300 glacial lakes with a total area of 65.17 km2 were detected by the proposed method. The area of the minimum glacial lake that can be extracted is 160 m2 (less than 6×6 pixels). This method has advantages in small glacial lake extraction and refined outline detection, which can be applied to extracting glacial lakes in the high-Asia region with high-resolution images.
合写作者：Rumeng Pan,Meng Pan
第一作者：Yungang Cao
论文类型：SCI
通讯作者：Xueqin Bai
学科门类：工学
文献类型：J
卷号：18
页面范围：153–168
是否译文：否
发表时间：2024-01-08
收录刊物：SCI