A Multi-Scale Content-Structure Feature Extraction Network Applied to Gully Extraction

doi:10.3390/rs16193562

	A Multi-Scale Content-Structure Feature Extraction Network Applied to Gully Extraction
	Dong, Feiyang 1,2; Jin, Jizhong 1,2; Li, Lei 1,2; Li, Heyang 1,2; Zhang, Yucheng 1
	2024-10-01
发表期刊	REMOTE SENSING
卷号	16 期号:19 页码:17
摘要	Black soil is a precious soil resource, yet it is severely affected by gully erosion, which is one of the most serious manifestations of land degradation. The determination of the location and shape of gullies is crucial for the work of gully erosion control. Traditional field measurement methods consume a large amount of human resources, so it is of great significance to use artificial intelligence techniques to automatically extract gullies from satellite remote sensing images. This study obtained the gully distribution map of the southwestern region of the Dahe Bay Farm in Inner Mongolia through field investigation and measurement and created a gully remote sensing dataset. We designed a multi-scale content structure feature extraction network to analyze remote sensing images and achieve automatic gully extraction. The multi-layer information obtained through the resnet34 network is input into the multi-scale structure extraction module and the multi-scale content extraction module designed by us, respectively, obtained richer intrinsic information about the image. We designed a structure content fusion network to further fuse structural features and content features and improve the depth of the model's understanding of the image. Finally, we designed a muti-scale feature fusion module to further fuse low-level and high-level information, enhance the comprehensive understanding of the model, and improve the ability to extract gullies. The experimental results show that the multi-scale content structure feature extraction network can effectively avoid the interference of complex backgrounds in satellite remote sensing images. Compared with the classic semantic segmentation models, DeepLabV3+, PSPNet, and UNet, our model achieved the best results in several evaluation metrics, the F1 score, recall rate, and intersection over union (IoU), with an F1 score of 0.745, a recall of 0.777, and an IoU of 0.586. These results proved that our method is a highly automated and reliable method for extracting gullies from satellite remote sensing images, which simplifies the process of gully extraction and provides us with an accurate guide to locate the location of gullies, analyze the shape of gullies, and then provide accurate guidance for gully management.
关键词	semantic segmentation gully extraction convolutional neural networks remote sensing
DOI	10.3390/rs16193562
收录类别	SCI
语种	英语
资助项目	Innovation Funding of the Institute of Computing Technology, Chinese Academy of Sciences ; [E261030]
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:001332840500001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39534
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Yucheng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Dong, Feiyang,Jin, Jizhong,Li, Lei,et al. A Multi-Scale Content-Structure Feature Extraction Network Applied to Gully Extraction[J]. REMOTE SENSING,2024,16(19):17.
APA	Dong, Feiyang,Jin, Jizhong,Li, Lei,Li, Heyang,&Zhang, Yucheng.(2024).A Multi-Scale Content-Structure Feature Extraction Network Applied to Gully Extraction.REMOTE SENSING,16(19),17.
MLA	Dong, Feiyang,et al."A Multi-Scale Content-Structure Feature Extraction Network Applied to Gully Extraction".REMOTE SENSING 16.19(2024):17.