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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

A Novel Fuzzy-Based Remote Sensing Image Segmentation Method.

Barbara Cardone¹, Ferdinando Di Martino^1,2, Vittorio Miraglia¹

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

Sensors (Basel, Switzerland)

|December 23, 2023

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:

FGFCM RSIS TCR fuzzy clustering image segmentation remote 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.