气候变化领域集成服务门户(CCPortal): Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping

CCPortal

DOI	10.1016/j.rse.2020.111945
	Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping
	Zhang Y.; Chen G.; Vukomanovic J.; Singh K.K.; Liu Y.; Holden S.; Meentemeyer R.K.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. In this study, we developed a Recurrent Shadow Attention Model (RSAM), capitalizing on state-of-the-art deep learning architectures, to retrieve fine-scale land-cover classes within cast and self shadows along the urban-rural gradient. The RSAM differs from the other existing shadow removal models by progressively refining the shadow detection result with two attention-based interacting modules – Shadow Detection Module (SDM) and Shadow Classification Module (SCM). To facilitate model training and validation, we also created a Shadow Semantic Annotation Database (SSAD) using the 1 m resolution (National Agriculture Imagery Program) NAIP aerial imagery. The SSAD comprises 103 image patches (500 × 500 pixels each) containing various types of shadows and six major land-cover classes – building, tree, grass/shrub, road, water, and farmland. Our results show an overall accuracy of 90.6% and Kappa of 0.82 for RSAM to extract the six land-cover classes within shadows. The model performance was stable along the urban-rural gradient, although it was slightly better in rural areas than in urban centers or suburban neighborhoods. Findings suggest that RSAM is a robust solution to eliminate the effects in high-resolution mapping both from cast and self shadows that have not received equal attention in previous studies. © 2020 Elsevier Inc.
英文关键词	Deep learning; High resolution; Recurrent Shadow Attention Model (RSAM); Shadow removal; Urban development patterns; Urban land-cover mapping
语种	英语
scopus关键词	Aerial photography; Agricultural robots; Antennas; Deep learning; Mapping; Semantics; High-resolution mapping; Learning architectures; Overall accuracies; Semantic annotations; Shadow classification; Urban environments; Urban land cover mappings; Urban-rural gradients; Rural areas; accuracy assessment; image resolution; land cover; mapping method; model; neighborhood; pixel; rural-urban comparison; urban region
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179244
作者单位	College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, United States; Laboratory for Remote Sensing and Environmental Change (LRSEC), Department of Geography and Earth Sciences, University of North Carolina at CharlotteNC 28223, United States; Department of Parks, Recreation and Tourism Management, North Carolina State University, Raleigh, NC 27695, United States; Global Research Institute, AidData, The College of William and Mary, Williamsburg, VA 23185, United States; Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, United States
推荐引用方式 GB/T 7714	Zhang Y.,Chen G.,Vukomanovic J.,et al. Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping[J],2020,247.
APA	Zhang Y..,Chen G..,Vukomanovic J..,Singh K.K..,Liu Y..,...&Meentemeyer R.K..(2020).Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping.Remote Sensing of Environment,247.
MLA	Zhang Y.,et al."Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping".Remote Sensing of Environment 247(2020).