Combinational shadow index for building shadow extraction in urban areas from Sentinel-2A MSI imagery

Abstract

Images from Multispectral Instrument (MSI) on Sentinel-2A are useful for studies of urban environments and their spatio-temporal changes. However, the frequent occurrence of building shadows in urban areas brings about great challenges in urban studies. Existing building shadow indices are not effective for Sentinel-2A MSI images due to that these indices do not make full use of the rich spectral information contained in Sentinel-2A. In this study, we propose a combinational shadow index (CSI) to address this challenge. In the formulation of CSI, three features, including the proposed shadow enhancement index (SEI), the normalized difference water index (NDWI) and the NIR band (B8), were combined to separate spectrally similar objects, such as building shadows, water and low albedo features at the Earth surface. The accuracy and robustness of CSI were tested by using six data sets from four cities, including Beijing, Shanghai, Guangzhou and Shenzhen in China. The performance of CSI was compared with three existing shadow indices, i.e., P algorithm, the normalized saturation-value difference index (NSVDI) and the shadow index (SI). Results show that CSI can detect building shadows with fine structures in both clear and cloudy images more effectively and worked well on large areas too. CSI can separate shadows from water and low albedo features as measured by a spectral discrimination index (SDI). Compared with the existing building shadow indices, CSI can improve the performance of building shadow detection by combining the feature information of SEI, NDWI and NIR band, and yielded satisfactory results for extracting building shadows from Sentinel-2A MSI imagery.