Water Body Extraction From Very High-Resolution Remote Sensing Imagery Using Deep U-Net and a Superpixel-Based Conditional Random Field Model

Research output: Contribution to journalArticle


Water body extraction (WBE) has attracted considerable attention in the field of remote sensing image analysis. Herein, we present an enhanced deep convolutional encoder-decoder (DCED) network (or Deep U-Net) specifically tailored to WBE from remote sensing images by applying superpixel segmentation and conditional random fields (CRFs). First, we preclassify the entire remote sensing image into the water and nonwater areas via Deep U-Net, using the results of class membership probabilities as the unary potential in the CRF model. The pairwise potential of CRF is defined by a linear combination of Gaussian kernels, which forms a fully connected neighbor structure. Next, regional restriction is incorporated into the approach to enhance the consistency of the connected area. We use the simple linear iterative clustering algorithm to generate superpixels and correct the binary classification results by calculating their average posterior probabilities. Finally, a highly efficient approximate inference algorithm, mean-field inference, is generated for the final model. The results from the experimental application to GaoFen-2 images and WorldView-2 images demonstrate that the proposed approach exhibits competitive quantitative and qualitative performance, which effectively reduces salt-and-pepper noise and retains the edge structures of water bodies. Compared to existing state-of-the-art methods, our proposed method achieves superior final results.


External organisations
  • Wuhan University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Other Earth and Related Environmental Sciences
  • Oceanography, Hydrology, Water Resources


  • Conditional random fields (CRFs), Deep U-Net, Feature extraction, Image segmentation, Kernel, regional restriction (RR), Remote sensing, Semantics, superpixel, water body extraction (WBE)., Water conservation, Water resources
Original languageEnglish
Pages (from-to)618-622
JournalIEEE Geoscience and Remote Sensing Letters
Issue number4
Early online date2018 Dec 12
Publication statusPublished - 2019
Publication categoryResearch