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A new two-step road extraction method in high resolution remote sensing images.

Wei Lu1, Xiaoying Shi1, Zhiping Lu1

  • 1Jiangsu Vocational College of Information Technology, Wuxi, China.

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This study introduces a two-step network for efficient road extraction from high-resolution remote sensing data. The method enhances road feature visibility and improves accuracy, achieving over 80% Mean Intersection over Union (MIOU).

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Geospatial Analysis

Background:

  • High-resolution remote sensing offers efficient, large-scale Earth observation, surpassing ground monitoring.
  • Traditional road extraction methods using limited image features struggle with complex real-world scenarios and occlusions.

Purpose of the Study:

  • To develop an advanced two-step network for accurate and robust road extraction from remote sensing imagery.
  • To improve upon existing road extraction techniques by enhancing feature representation and model stability.

Main Methods:

  • Optimized a pix2pix model for image translation to generate map-style images highlighting road features.
  • Proposed a novel FusionLinkNet model integrating DenseNet, ResNet, and LinkNet for stable road information fusion.

Main Results:

  • The optimized pix2pix model effectively enhances road visibility and mitigates occlusion issues.
  • FusionLinkNet demonstrated improved accuracy and learning rates in road extraction tasks.
  • Achieved a Mean Intersection over Union (MIOU) exceeding 80% on both DeepGlobe and Massachusetts road datasets.

Conclusions:

  • The proposed two-step network significantly advances road extraction capabilities using high-resolution remote sensing data.
  • The FusionLinkNet model offers a stable and accurate solution for identifying road networks, outperforming previous methods.