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1 Zhang, Y.; Chen, G.; Vukomanovic, J.; Singh, K. K.; Liu, Y.; Holden, S.; Meentemeyer, R. K.. 2020. Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping. Remote Sensing of Environment, 247:111945. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111945]
Land cover mapping ; Imagery ; Urban development ; Landscape ; Remote sensing ; Semantic standard ; Databases ; Models ; Suburban areas / USA / North Carolina / Raleigh / Durham / Chapel Hill
(Location: IWMI HQ Call no: e-copy only Record No: H049774)
(7.14 MB)
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.
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