Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models

Land cover mapping is essential for 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. On the natural images, deep learning has outperformed traditional machine learning on a range of tasks, including the image segmentation. On remote sensing images, recent studies are demonstrating successful application of specific deep learning models or their adaptations to particular small-scale mapping tasks (e.g., to classify wetland complexes). However, it is not readily clear which of the existing models for natural images are the best candidates to be taken for the particular remote sensing task and data. In this study, we answer that question for mapping the fundamental land cover classes using the satellite imaging radar data. We took ESA 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 Level-1 CORINE classes. We selected seven among the state-of-the-art semantic segmentation models so that they cover a diverse set of approaches. We used 14 ESA Sentinel-1 scenes acquired during the summer season in Finland, which are representative of the land cover in the country. Upon the benchmarking, all the models demonstrated solid performance. The best model, FC-DenseNet (Fully Convolutional DenseNets), achieved the overall accuracy of 90.7%. Overall, our results indicate that the semantic segmentation models are suitable for efficient wide-area mapping using satellite SAR imagery. Our results also provide baseline accuracy against which the newly proposed models should be evaluated and suggest the DenseNet-based models are the first candidate for this task.