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Automatic dense labeling of multispectral satellite images facilitates faster map update process. Water objects are essential elements of a geographic map. While modern dense labeling methods perform robust segmentation of such objects like roads, buildings, and vegetation, dense labeling of hydrographic regions remains a challenging problem. Water objects change their surface albedo, color, and reflection in different weather and different seasons. Moreover, rivers and lakes can change their boundaries after floods or droughts. Robust documentation of such seasonal changes is an essential task in the field of analysis of satellite imagery. Due to the high variance in water object appearance, their segmentation is usually performed manually by a human operator. Recent advances in machine learning have made possible robust segmentation of static objects such as buildings and roads. To the best of our knowledge, there is little research in the modern literature regarding dense labeling of water regions. This paper is focused on the development of a deep-learning-based method for dense labeling of hydrographic in aerial and satellite imagery. We use the GeoGAN framework and MobileNetV2 as the starting point for our research. The GeoGAN framework uses an aerial image as an input to generate pixel-level annotations of five object classes: building, low vegetation, high vegetation, road, and car. The GeoGAN framework leverages two deep learning approaches to ensure robust labeling: a generator with skip connections and Generative Adversarial Networks. A generator with skip connections performs image→label translation using feed-forward connections between convolutional and deconvolutional layers of the same depth. A GAN framework consists of two competing networks: a generator and a discriminator. The adversarial loss improves the quality of the resulting dense labeling. We made the following contributions to the GeoGAN framework: (1) new MobileNetV2-based generator, (2) adversarial loss function. We term the resulting framework as HydroGAN. We evaluate our HydroGAN model using a new HydroViews dataset focused on dense labeling of areas that are subject to severe flooding during the spring season. The evaluation results are encouraging and demonstrate that our HydroGAN model competes with the state-of-the-art models for dense labeling of aerial and satellite imagery. The evaluation demonstrates that our model can generalize from the training data to previously unseen samples. The developed HydroGAN model is capable of performing dense labeling of water objects in different seasons. We made our model publicly available.
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