Deep multi-feature learning architecture for water body segmentation from satellite images

Automatic water body extraction from satellite images of various scenes is a classical and challenging task in remote sensing and image interpretation. Convolutional neural network (CNN) has become prominent option for performing image segmentation task in remote sensing applications. However, CNN-based networks have non-trivial issues for segmenting such as: (1) blurring boundary pixels; (2) large number of trainable parameters; and (3) huge number of training samples. In this paper, we propose an end-to-end multi-feature based CNN architecture, called as W-Net, to perform water body segmentation. W-Net consists of contracting/expanding networks and inception layers. W-Net takes advantage of contracting network to capture context information while localization is achieved with expanding network. With these networks, W-Net is able to train on less number of images and extract water pixels accurately. Use of inception layers reduces computational burden within the network by decreasing total number of trainable parameters. W-Net incorporated two refinement modules to enhance predicted results which mitigate blurring effect and to inspect continuity of boundary pixels. Dataset consisting 2671 images with manually annotated ground truths are built to validate performance and effectiveness of our proposed method. In addition, we evaluated our method on crack detection dataset where W-Net achieved competitive performance with Deepcrack. W-Net accomplished excellent performance on the water body dataset (I∕U=0.9434 and F−score=0.9509). •Water body segmentation is crucial for many applications.•W-Net consists of inception blocks at both encoder and decoder paths.•Use of asymmetric convolution reduces trainable parameters within the network.•Refinement modules enhances predicted image and identifies boundary pixels accurately.