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

doi:10.3390/rs14194729

	Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark
	Zhang, Xinyu 1,2; Jiang, Yu 3,4; Wang, Lizhe 1,2; Han, Wei 1,2; Feng, Ruyi 1,2; Fan, Runyu 1,2; Wang, Sheng 1,2
	2022-10-01
发表期刊	REMOTE SENSING
卷号	14 期号:19 页码:16
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.
关键词	road extraction remote sensing high-resolution remote sensing semantic segmentation transformer
DOI	10.3390/rs14194729
收录类别	SCI
语种	英语
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
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000867136300001
出版者	MDPI
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19795
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.China Univ Geosci, Sch Comp Sci, Wuhan 430078, Peoples R China 2.China Univ Geosci, Key Lab Intelligent Geoinformat Proc, Wuhan 430078, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, 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.