Optimized Fully Convolutional Neural Network Encoder for Water Detection in SAR Images

Visual interpretation of Synthetic Aperture Radar (SAR) images plays an important role in remote sensing mainly because SAR images enable consistent monitoring in any lighting, weather, and cloud-cover conditions. An important application of SAR visualization is water detection. We introduce a Fully Convolutional Neural Network (FCN) Encoder to detect water in Sentinel-1 SAR images. Our FCN Encoder identifies water by the intensity of each pixel and also learns the spatial information of neighborhood pixels. We apply our method on standard benchmarks and real-world SAR images. The results are assessed both visually and from the point of view of classification accuracy. Compared with other classifiers, our FCN Encoder is more accurate. From visual inspection of the Seattle water detection result, the FCN Encoder produces a very clear (smooth) output. The results show that the FCN Encoder, trained with a harder dataset and hyperparameter optimization, improves significantly its generalization performance. In a real-world application, for the prediction phase, the FCN Encoder is about 40 times faster than a Convolutional Neural Network (CNN) with sliding window.