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Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification.

Shuang Hao1, Yuhuan Cui1, Jie Wang2

  • 1School of Natural Science, Anhui Agricultural University, Hefei 230036, China.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary

Selecting optimal segmentation scales for object-based image analysis (OBIA) is challenging. This study introduces a novel method using land object average areas to determine the best segmentation scale for high-resolution land cover classification.

Keywords:
CART modelOBIAWorldview-3scale

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Image Processing

Background:

  • Object-based image analysis (OBIA) is crucial for high-spatial-resolution imagery but faces challenges in optimal scale selection for image segmentation.
  • Effective segmentation parameter selection is vital for accurate land cover classification and target recognition.

Purpose of the Study:

  • To present an effective approach for selecting optimal segmentation scales in OBIA based on land object average areas.
  • To address the complexity of choosing appropriate segmentation parameters for high-resolution image analysis.

Main Methods:

  • Utilized 20 different segmentation scales for image segmentation of WorldView-3 data.
  • Employed the Classification and Regression Tree (CART) model for image classification using spectral, texture, vegetation, and spatial features.
  • Estimated average areas of land objects from segmentation results to determine optimal scale parameters.

Main Results:

  • Demonstrated a strong correlation between segmentation scales and the average area of land objects.
  • Validated the effectiveness of the land object average area-based method for optimal segmentation scale selection.
  • Confirmed that the proposed method can determine optimal segmentation scales tailored to different land objects.

Conclusions:

  • The area-based segmentation scale selection method is suitable for determining optimal parameters for diverse land objects in OBIA.
  • This approach offers a practical solution to the scale selection problem in image segmentation for land cover classification.
  • The study suggests potential for extending this method to various image segmentation algorithms and applications.