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An Efficient Building Extraction Method from High Spatial Resolution Remote Sensing Images Based on Improved Mask

Lili Zhang1, Jisen Wu1, Yu Fan1

  • 1College of Computer and Information Engineering, Hohai University, Nanjing 211100, China.

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Summary

This study introduces Mask R-CNN Fusion Sobel, a novel framework for accurate building extraction from remote sensing images. The method enhances deep learning with edge detection, achieving superior results in complex urban environments.

Keywords:
building extractionconvolutional neural networkshigh-resolution remote sensing imagemask R-CNN

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional building extraction methods rely on artificial features, facing challenges with building diversity and complexity.
  • Deep learning approaches have emerged as a promising alternative for improving building extraction accuracy.

Purpose of the Study:

  • To propose an improved building extraction framework integrating deep learning and edge detection for high-resolution remote sensing images.
  • To address limitations in current methods regarding false positives, missed extractions, and object integrity.

Main Methods:

  • A novel framework, Mask R-CNN Fusion Sobel, combines Mask R-CNN for semantic feature extraction and pixel classification with the Sobel edge detection algorithm for precise edge segmentation.
  • The method employs a fusion algorithm to integrate the outputs of the convolutional neural network and edge detection for final building extraction.

Main Results:

  • The proposed framework achieved an average Intersection over Union (IOU) of 88.7% and an average Kappa of 87.8% on high-resolution GF-2 satellite imagery.
  • Experimental results demonstrate superior accuracy compared to classical methods, particularly for complex building structures.

Conclusions:

  • The Mask R-CNN Fusion Sobel framework offers a robust and accurate solution for building extraction from high-resolution remote sensing data.
  • The integration of deep learning with edge detection effectively handles the complexity and diversity of urban buildings.