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A Novel Fuzzy-Based Remote Sensing Image Segmentation Method.

Barbara Cardone1, Ferdinando Di Martino1,2, Vittorio Miraglia1

  • 1Department of Architecture, University of Naples Federico II, Via Toledo 402, 80134 Naples, Italy.

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|December 23, 2023
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Summary
This summary is machine-generated.

This study introduces a fast, fuzzy-based image segmentation framework for remote sensing data, improving accuracy and reliability while reducing computational complexity. The GIS-based platform efficiently segments multiband images for applications like land use classification.

Keywords:
FGFCMRSISTCRfuzzy clusteringimage segmentationremote sensing

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Area of Science:

  • Geosciences
  • Computer Science
  • Remote Sensing

Background:

  • Image segmentation partitions images into meaningful regions, crucial for analyzing remotely sensed data in applications like land use classification.
  • Existing hybrid segmentation techniques using metaheuristics offer improved accuracy but suffer from high computational costs.
  • Addressing computational complexity is vital for applying segmentation to large-scale, high-resolution remote sensing imagery.

Purpose of the Study:

  • To propose a novel fuzzy-based image segmentation framework for remotely sensed images.
  • To enhance segmentation accuracy and reliability while mitigating high computational complexity.
  • To implement the framework within a GIS-based platform for practical application and evaluation.

Main Methods:

  • Implementation of the Fast Generalized Fuzzy c-means algorithm for pixel spatial relation detection.
  • Utilization of the Triple Center Relation validity index to determine the optimal number of clusters.
  • Development of a framework to analyze composite indices from multiband remote sensing data and generate thematic maps with reliability assessment.

Main Results:

  • The proposed fuzzy-based framework achieves high computational speed, enabling application to massive high-resolution remote sensing images.
  • Segmentation results demonstrate consistency with expert-defined morphological and urban characteristics of the study area.
  • The method provides a reliable estimation of classification accuracy based on pixel membership degrees.

Conclusions:

  • The developed fuzzy-based segmentation framework offers an efficient and reliable solution for processing remotely sensed imagery.
  • The GIS integration and computational speed make the method suitable for large-scale remote sensing applications.
  • This approach effectively balances segmentation accuracy, reliability, and computational performance.