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Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark

文献类型：期刊论文

作者Zhang, Xinyu3,4; Jiang, Yu1,2; Wang, Lizhe3,4; Han, Wei3,4; Feng, Ruyi3,4; Fan, Runyu3,4; Wang, Sheng3,4
刊名REMOTE SENSING
出版日期2022-10-01
卷号14期号:19页码:16
关键词road extraction remote sensing high-resolution remote sensing semantic segmentation transformer
DOI10.3390/rs14194729
英文摘要Mountain roads are of great significance to traffic navigation and military road planning. Extracting mountain roads based on high-resolution remote sensing images (HRSIs) is a hot spot in current road extraction research. However, massive terrain objects, blurred road edges, and sand coverage in complex environments make it challenging to extract mountain roads from HRSIs. Complex environments result in weak research results on targeted extraction models and a lack of corresponding datasets. To solve the above problems, first, we propose a new dataset: Road Datasets in Complex Mountain Environments (RDCME). RDCME comes from the QuickBird satellite, which is at an elevation between 1264 m and 1502 m with a resolution of 0.61 m; it contains 775 image samples, including red, green, and blue channels. Then, we propose the Light Roadformer model, which uses a transformer module and self-attention module to focus on extracting more accurate road edge information. A post-process module is further used to remove incorrectly predicted road segments. Compared with previous related models, the Light Roadformer proposed in this study has higher accuracy. Light Roadformer achieved the highest IoU of 89.5% for roads on the validation set and 88.8% for roads on the test set. The test on RDCME using Light Roadformer shows that the results of this study have broad application prospects in the extraction of mountain roads with similar backgrounds.
资助项目National Natural Science Foundation of China[U21A2013] ; National Natural Science Foundation of China[42201415] ; National Natural Science Foundation of China[41925007] ; Hubei Natural Science Foundation of China[2019CFA023] ; Fundamental Research Founds for the Central Universities, China University of Geosciences (Wuhan)[162301212697]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000867136300001
源URL[http://119.78.100.204/handle/2XEOYT63/19794]  
专题中国科学院计算技术研究所期刊论文
通讯作者Han, Wei
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
3.China Univ Geosci, Key Lab Intelligent Geoinformat Proc, Wuhan 430078, Peoples R China
4.China Univ Geosci, Sch Comp Sci, Wuhan 430078, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Xinyu,Jiang, Yu,Wang, Lizhe,et al. Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark[J]. REMOTE SENSING,2022,14(19):16.
APA Zhang, Xinyu.,Jiang, Yu.,Wang, Lizhe.,Han, Wei.,Feng, Ruyi.,...&Wang, Sheng.(2022).Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark.REMOTE SENSING,14(19),16.
MLA Zhang, Xinyu,et al."Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark".REMOTE SENSING 14.19(2022):16.

入库方式： OAI收割

来源：计算技术研究所

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