Deepcloud - A Fully Convolutionnal Neural Network for Cloud and Shadow Masking in Optical Satellite Images

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    Abstract

    Many cloud and shadow detection methods have been proposed already, but improvements can be made on accuracy or automation. In this study, we propose a Fully Convolutional Network model for the detection of clouds and shadows in optical satellite images. The proposed model was trained on 165 Landsat images in Finland, and tested on an independent set of images. The cloud and shadow detection accuracy reached 95%, outperforming both quantitatively and qualitatively a selection of other deep learning architectures.
    Original languageEnglish
    Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages2107-2110
    Number of pages4
    ISBN (Electronic)978-1-5386-7150-4 , 978-1-5386-7149-8
    ISBN (Print)978-1-5386-7151-1
    DOIs
    Publication statusPublished - 5 Nov 2018
    MoE publication typeNot Eligible
    EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
    Duration: 22 Jul 201827 Jul 2018

    Conference

    ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
    Abbreviated titleIGARSS
    CountrySpain
    CityValencia
    Period22/07/1827/07/18

    Keywords

    • Remote sensing
    • Clouds
    • Satellites
    • Artificial satellites
    • Earth
    • Optical imaging
    • Cloud and shadow masking
    • optical images
    • deep learning
    • fully convolutional network
    • Landsat

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