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An STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data.

Lijing Bu1, Dong Dai1, Liying Tu2

  • 1College of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, People's Republic of China.

Royal Society Open Science
|November 25, 2022
PubMed
Summary

This study introduces a novel method using spatio-temporal remote sensing and point-of-interest data to accurately extract urban built-up areas, improving classification accuracy by addressing noise in night-time light data.

Keywords:
extraction of built-up areahuman settlement indexlandsatmulti-sourcenight-time light remote sensing imagerandom forest

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Area of Science:

  • Earth Observation
  • Urban Remote Sensing
  • Geographic Information Systems

Background:

  • Urban built-up area extraction is crucial for understanding urbanization.
  • Existing methods using Human Settlement Index (HSI) are affected by noise, like light spillover.
  • Multi-source remote sensing data integration is key to improving accuracy.

Purpose of the Study:

  • To develop a high-precision method for urban built-up area extraction.
  • To improve the accuracy of Human Settlement Index (HSI) by correcting noise.
  • To integrate spatio-temporal remote sensing and point-of-interest (POI) data for enhanced classification.

Main Methods:

  • Proposed a fuzzy c-means spatio-temporal point (FCM-STP) index to correct light spillover using Luojia-1 and POI data.
  • Updated HSI to spatio-temporal point human settlement index (STP-HSI).
  • Employed a random forest algorithm with NDVI, NDBI, STP-HSI, and texture features for extraction.

Main Results:

  • The proposed STP-HSI method significantly improved urban built-up area extraction accuracy.
  • Overall accuracy increased by up to 7.52% and kappa coefficient by up to 14% compared to single-source data.
  • STP-HSI showed a maximum contribution rate increase of 25.74% compared to the original HSI.

Conclusions:

  • The developed spatio-temporal point human settlement index (STP-HSI) method is effective for high-precision urban built-up area extraction.
  • Integration of multi-source data and noise correction enhances classification accuracy.
  • The method demonstrates feasibility and superiority over existing approaches for urban monitoring.