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A Method for Extracting Building Information from Remote Sensing Images Based on Deep Learning.

Lianying Li1, Xi Chen2, Lianchao Li3

  • 1School of Art and Design, Harbin University, Harbin, Heilongjiang 150086, China.

Computational Intelligence and Neuroscience
|October 24, 2022
PubMed
Summary

This study introduces a new deep learning method for remote sensing image analysis, improving building information extraction and object contour accuracy. The approach enhances segmentation performance and efficiency for diverse remote sensing applications.

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Artificial Intelligence

Background:

  • Semantic segmentation of remote sensing images is crucial for information extraction.
  • Current algorithms struggle with precise object contour capture and inter-object interaction analysis.

Purpose of the Study:

  • To propose a deep learning-based method for enhanced building information extraction from remote sensing images.
  • To improve the accuracy and efficiency of object contour capture and dataset fitting.

Main Methods:

  • Integration of the DeepLabv3+ semantic segmentation model with Mixconv2d for multi-scale feature recognition.
  • Application of Rdrop Loss regularization to enhance contour accuracy and dataset consistency across different resolutions.

Main Results:

  • The proposed method demonstrates improved algorithm efficiency and result accuracy in remote sensing image segmentation.
  • Effective capture of object contours and improved segmentation performance were observed.

Conclusions:

  • The developed deep learning approach offers a robust solution for semantic segmentation in remote sensing.
  • The method effectively addresses limitations in contour detection and information extraction from remote sensing data.