Classification of Wide-Area SAR Mosaics: Deep Learning Approach for Corine Based Mapping of Finland Using Multitemporal Sentinel-1 Data

Abstract

Here, we examine a deep learning approach to perform land cover classification using country-wide SAR mosaics compiled using multitemporal Sentinel-1 imagery. We capitalize on our earlier study [1], demonstrating the suitability of deep learning models for land cover mapping using satellite C-band SAR images. A set of SAR mosaics compiled from consecutive Sentinel-1 IW mode acquisitions covering the whole territory of Finland was used in production of the whole-country land cover map. The imagery were used as an input to the state-of-the-art deep-learning model for semantic segmentation called FC-DenseNet. This model was pre-trained on the ImageNet dataset and further fine-tuned in this study. CORINE land cover map was used as a reference, and the model was trained to distinguish between 5 Level-1 CORINE classes. Upon the evaluation and benchmarking, we found that the FC-DenseNet model is able to achieve nearly 90% overall classification accuracy. These results indicate the suitability of deep learning approaches to support efficient operational wide-area mapping using satellite SAR imagery.