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Review of shadow detection and de-shadowing methods in remote sensing

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Abstract

Shadow is one of the major problems in remotely sensed imagery which hampers the accuracy of information extraction and change detection. In these images, shadow is generally produced by different objects, namely, cloud, mountain and urban materials. The shadow correction process consists of two steps: detection and de-shadowing. This paper reviews a range of techniques for both steps, focusing on urban regions (urban shadows), mountainous areas (topographic shadow), cloud shadows and composite shadows. Several issues including the problems and the advantages of those algorithms are discussed. In recent years, thresholding and recovery techniques have become important for shadow detection and de-shadowing, respectively. Research on shadow correction is still an important topic, particularly for urban regions (in high spatial resolution data) and mountainous forest (in high and medium spatial resolution data). Moreover, new algorithms are needed for shadow correction, especially given the advent of new satellite images.

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  1. Institute of Agriculture Remote Sensing and Information Technology, College of Environment and Natural Resource, Zhejiang University, Hangzhou, 310058, China

    AmirReza Shahtahmassebi, Ning Yang, Ke Wang & Zhangquan Shen

  2. Department of Geography, Michigan State University, East Lansing, MI, 48823, USA

    Nathan Moore

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Correspondence to Ke Wang.

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Foundation item: Under the auspices of National Technology Research and Development Program of China (No. 2006BAJ05A02), National Natural Science Foundation of China (No. 31172023)

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Shahtahmassebi, A., Yang, N., Wang, K. et al. Review of shadow detection and de-shadowing methods in remote sensing. Chin. Geogr. Sci. 23, 403–420 (2013). https://doi.org/10.1007/s11769-013-0613-x

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