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    TanDEM-X Forest Mapping using Convolutional Neural Networks

    TanDEM-X Forest Mapping using Convolutional Neural Networks

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    In this work we face the problem of forest mapping from TanDEM-X data by means of Convolutional Neural Networks (CNNs). Our study aims to highlight the relevance of domain-related features for the extraction of the information of interest, thanks to their joint nonlinear processing through CNN. In particular, we focus on the main InSAR features as the backscatter, coherence, and volume decorrelation, as well as the acquisition geometry through the local incidence angle. By using different state-of-the-art CNN architectures, our experiments consistently demonstrate the great potential of deep learning in data fusion for information extraction in the context of synthetic aperture radar signal processing, and specifically for the task of forest mapping from TanDEM-X images. We compare three state-of-the-art CNN architectures, such as ResNet, DenseNet, and U-Net, obtaining a large performance gain over the baseline approach for all of them, with the U-Net solution being the most effective one.

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    Titel :

    TanDEM-X Forest Mapping using Convolutional Neural Networks

    Beteiligte:
    Mazza, Antonio (Autor:in) / Sica, Francescopaolo (Autor:in) / Rizzoli, Paola (Autor:in) / Scarpa, Giuseppe (Autor:in)
    Erschienen in:

    Remote Sensing ; 11 , 2980

    Erscheinungsdatum :

    2019-12-12

    DOI :

    https://doi.org/10.3390/rs11242980

    Medientyp :

    Aufsatz (Zeitschrift)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

    Schlagwörter :

    image segmentation , forest classification , data fusion , Synthetic aperture radar , target detection , Convolutional Neural Network , digital elevation model

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