Deeply supervised convolutional neural network for shadow detection based on a novel aerial shadow imagery dataset

• DOI: 10.1016/j.isprsjprs.2020.07.016
• Publication Date: 2020-08-06T09:54:23Z
• Authors: Shuang Luo; Huifang Li; Huanfeng Shen
• Keywords: Discriminative model; Aerial image; Feature (linguistics)

Abstract Shadow detection is an essential work for remote sensing image analysis, as the presence of shadows in high resolution images not only degrades the radiometric information but also disturbs the image interpretation. In this paper, a convolutional neural network (CNN) based shadow detection framework for aerial remote sensing images is presented. We construct a publicly available Aerial Imagery dataset for Shadow Detection (AISD), which is the first aerial shadow imagery dataset, as far as we know. Based on AISD, we propose a novel Deeply Supervised convolutional neural network for Shadow Detection (DSSDNet). To solve the insufficient feature extraction problem of shadows, the DSSDNet model is designed to include two steps: (1) an encoder-decoder residual (EDR) structure is adopted to extract multi-level and discriminative shadow features; (2) a deeply supervised progressive fusion (DSPF) process is then imposed on EDR to further boost the detection performance by directly guiding the training of the network and fuse adjacent feature maps progressively. The proposed DSSDNet is compared with several state-of-the-art methods in both qualitative and quantitative analysis. Results show that the proposed DSSDNet is more accurate, and more consistent to the shape of the objects casting shadows, with the average F-score being 91.79% on the testing images.