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Simple and effective cloud- and shadow-detection algorithms for Landsat and Worldview images

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Abstract

Many conventional cloud- and shadow-detection algorithms require meta-data such as sun angle and date of image collection. Moreover, detection results can vary a lot in actual images. We present simple and effective algorithms that do not require meta-data for detecting clouds and shadows in Landsat and Worldview images. Comparison with existing state-of-the-art algorithms, including a deep learning-based algorithm as well as a commercial algorithm, using actual satellite images, shows that the simple algorithms have comparable or even better performance than existing algorithms.

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Acknowledgements

This research was supported by DARPA under contract number 140D6318C0043. The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the US Government.

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Authors and Affiliations

  1. Applied Research LLC, Rockville, MD, USA

    Chiman Kwan, Leif Hagen & Bryan Chou

  2. Old Dominion University, Norfolk, VA, USA

    Daniel Perez, Jiang Li & Yuzhong Shen

  3. Digital Globe, Inc, Westminster, CO, USA

    Krysztof Koperski

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  1. Chiman Kwan
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  2. Leif Hagen
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  3. Bryan Chou
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  4. Daniel Perez
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  5. Jiang Li
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  6. Yuzhong Shen
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  7. Krysztof Koperski
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Correspondence to Chiman Kwan.

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Kwan, C., Hagen, L., Chou, B. et al. Simple and effective cloud- and shadow-detection algorithms for Landsat and Worldview images. SIViP 14, 125–133 (2020). https://doi.org/10.1007/s11760-019-01532-2

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