A new optimization approach based on neural architecture search to enhance deep U-Net for efficient road segmentation

This publication has 38 references indexed in Scilit:

• RL-DARTS: Differentiable neural architecture search via reinforcement-learning-based meta-optimizer
  Knowledge-Based Systems, 2021
• BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery
  ISPRS Journal of Photogrammetry and Remote Sensing, 2020
• Land-cover classification with high-resolution remote sensing images using transferable deep models
  Remote Sensing of Environment, 2020
• NAS-Unet: Neural Architecture Search for Medical Image Segmentation
  IEEE Access, 2019
• A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images
  Remote Sensing Letters, 2019
• Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images
  ISPRS Journal of Photogrammetry and Remote Sensing, 2017
• 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
  2016
• Semi-Automated Road Detection From High Resolution Satellite Images by Directional Morphological Enhancement and Segmentation Techniques
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012
• Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images
  Pattern Recognition Letters, 2010
• Hierarchical optimization: An introduction
  Annals of Operations Research, 1992