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[IEEE 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - Las Vegas, NV (2018.4.8-2018.4.10)] 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - A Reflectance Based Method For Shadow Detection and Removal
摘要： Shadows are common aspect of images and when left undetected can hinder scene understanding and visual processing. We propose a simple yet effective approach based on reflectance to detect shadows from single image. An image is first segmented and based on the reflectance, illumination and texture characteristics, segments pairs are identified as shadow and non-shadow pairs. The proposed method is tested on two publicly available and widely used datasets. Our method achieves higher accuracy in detecting shadows compared to previous reported methods despite requiring fewer parameters. We also show results of shadow-free images by relighting the pixels in the detected shadow regions.
关键词： image enhancement,shadow removal,reflectance classifier,shadow detection
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Cloud Shadow Removal Based on Cloud Transmittance Estimation
摘要： This paper proposes a method of cloud shadow removal for multispectral images to retrieve the ground reflectance of areas shadowed by clouds. Cloud shadows are cast when incident direct solar irradiance gets attenuated by clouds. To retrieve the ground reflectance of the shadowed pixels, it is required to estimate pixel-wise attenuation factor for the solar irradiance. Unlike conventional methods, the proposed technique takes the physical model of cloud shadow formation into account to accurately estimate the attenuation factors. According to the physical model, the factors are derived from the transmittance of an occluding cloud. Visual and quantitative results demonstrate that the proposed method outperforms the well-known de-shadowing algorithm. The average correlation coefficient of the corrected image with a reference image is improved from 0.45 to 0.75 by the proposed method as compared to the conventional method. Further, the average spectral angle with a reference image is improved by 10%.
关键词： shadow removal,Cloud,physical model,spectral unmixing
更新于2025-09-10 09:29:36