Spectral-Spatial Topographic Shadow Detection from Sentinel-2A MSI Imagery Via Convolutional Neural Networks

Abstract

Accurate detection of topographic shadows is of great importance, since topographic shadowing is an inevitable hamper for the interpretation of remotely sensed images covered mountainous areas. In this paper, a novel method is proposed for effective and efficient topographic shadow detection for the images obtained from Sentinel-2A multispectral imager (MSI) by combining both the spectral and spatial information. In this method, four feature indices were firstly extracted from the original Sentinel-2A spectral bands to capture the essential spectral characteristics. Specifically, we constructed a topographic shadow index (TSI) to enhance topographic shadows, focusing on the spectral signatures of shadows in Sentinel-2A MSI imagery. To further enhance the difference between shadows and other objects, the TSI is combined with the first component of the principal component analysis (FPC), the soil-adjusted vegetation index (SAVI) and the normalized water index (NDWI), representing topographic shadows, rocks, vegetation and water, respectively. Finally, a convolutional neural network (CNN) was used by operating directly on indices input due to its remarkable classification performance, which exploits the spatial contextual information and spectral features for effective topographic extraction. Our experiments with one Sentinel-2A image show that the proposed approach has led to satisfactory performances, with few errors in shadow maps and insignificant confusion with spectral-similar land covers.