  Land cover mapping is essential to monitoring the environment and
understanding the effects of human activities on it. The automatic approaches
to land cover mapping (i.e., image segmentation) mostly used traditional
machine learning that requires heuristic feature design. On natural images,
deep learning has outperformed traditional machine learning approaches for
image segmentation. On remote sensing images, recent studies demonstrate
successful applications of specific deep learning models to small-scale land
cover mapping tasks (e.g., to classify wetland complexes). However, it is not
readily clear which of the existing models are the best candidates for which
remote sensing task. In this study, we answer that question for mapping the
fundamental land cover classes using satellite radar data. We took Sentinel-1
C-band SAR images available at no cost to users as representative data. CORINE
land cover map was used as a reference, and the models were trained to
distinguish between the 5 major CORINE classes. We selected seven among the
state-of-the-art semantic segmentation models so that they cover a diverse set
of approaches: U-Net, DeepLabV3+, PSPNet, BiSeNet, SegNet, FC-DenseNet, and
FRRN-B. The models were pre-trained on the ImageNet dataset and further
fine-tuned in this study. All the models demonstrated solid performance with
overall accuracy between 87.9% and 93.1%, and with good to a very good
agreement (kappa statistic between 0.75 and 0.86). The two best models were
FC-DenseNet and SegNet, with the latter having a much smaller inference time.
Overall, our results indicate that the semantic segmentation models are
suitable for efficient wide-area mapping using satellite SAR imagery and also
provide baseline accuracy against which the newly proposed models should be
evaluated.
