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Updated: May 31, 2025

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

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Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm.

Mirza Muhammad Waqar¹, Heein Yang¹, Rahmi Sukmawati¹

¹Satellite Image Application Team, CONTEC, Daejeon 34074, Republic of Korea.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

This summary is machine-generated.

This study introduces a robust method for detecting urban changes using KOMPSAT-5 time-series synthetic aperture radar (SAR) data. The approach achieves 92% accuracy, enhancing change detection capabilities for urban monitoring.

Keywords:

KOMPSAT-5 amplitude change detection change detection statistical homogeneous pixels (SHP)

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Area of Science:

Remote Sensing
Geospatial Analysis
Earth Observation

Background:

Amplitude Change Detection (ACD) and Coherent Change Detection (CCD) are common for synthetic aperture radar (SAR) change detection.
Time-series SAR data often suffer from noise and variability, complicating analysis and requiring robust preprocessing.
Spatial variability and environmental factors can affect the accuracy of traditional SAR change detection methods.

Purpose of the Study:

To develop and validate a robust methodology for urban change detection using KOMPSAT-5 time-series SAR data.
To address noise and variability in SAR data through comprehensive preprocessing.
To enhance the reliability and accuracy of SAR-based urban change detection for monitoring applications.

Main Methods:

Implemented a preprocessing framework including coregistration, radiometric terrain correction, normalization, and speckle filtering.
Utilized statistical homogeneous pixels (SHPs) for stable target identification and coherence-based analysis for temporal decorrelation.
Applied adaptive thresholding, morphological operations, and small-segment removal for change refinement and noise mitigation.

Main Results:

Achieved an overall accuracy of 92% in detecting urban changes, validated via confusion matrix analysis.
Successfully identified urban changes, demonstrating the effectiveness of the developed methodology.
The preprocessing and analysis framework proved reliable for consistent and accurate SAR data interpretation.

Conclusions:

The developed methodology offers a reliable approach for urban change detection using KOMPSAT-5 time-series SAR data.
The findings highlight the potential of KOMPSAT-5 data for post-disaster monitoring and urban planning.
Further research on InSAR orbit stability could enhance detection precision and broaden SAR time-series applications.