Automatic Water-Body Segmentation From High-Resolution Satellite Images via Deep Networks

Water-body segmentation is an important issue in remote sensing and image interpretation. Classic methods for counteracting this problem usually include the construction of index features by combining different spectra, however, these methods are essentially rule-based and fail to take advantage of context information. Additionally, as the quality of image resolution improves, these methods are proved to be inadequate. With the rise of convolutional neural networks (CNN), the level of research about segmentation has taken a huge leap, but the field is still facing an increasing demand for data and the problem of blurring boundaries. In this letter, a new segmentation network called restricted receptive field deconvolution network (RRF DeconvNet) is proposed, with which to extract water bodies from high-resolution remote sensing images. Compared with natural images, remote sensing images have a weaker pixel neighborhood relativity; in consideration of this challenge, an RRF DeconvNet compresses the redundant layers in the original DeconvNet and no longer relies on a pretrained model. In addition, to tackle the blurring boundaries that occur in CNN, a new loss function called edges weighting loss is proposed to train segmentation networks, which has been shown to significantly sharpen the segmentation boundaries in results. Experiments, based on Google Earth images for water-body segmentation, are presented in this letter to prove our method.