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

For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additional...

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Main Authors: Mirza Muhammad Waqar, Heein Yang, Rahmi Sukmawati, Sung-Ho Chae, Kwan-Young Oh
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Sensors
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Online Access:https://www.mdpi.com/1424-8220/25/2/583
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author Mirza Muhammad Waqar
Heein Yang
Rahmi Sukmawati
Sung-Ho Chae
Kwan-Young Oh
author_facet Mirza Muhammad Waqar
Heein Yang
Rahmi Sukmawati
Sung-Ho Chae
Kwan-Young Oh
author_sort Mirza Muhammad Waqar
collection DOAJ
description For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations. Although ACD and CCD techniques can detect changes, spatial variability outside the primary target area introduces complexity into the analysis. This study presents a robust change detection methodology designed to identify urban changes using KOMPSAT-5 time-series data. A comprehensive preprocessing framework—including coregistration, radiometric terrain correction, normalization, and speckle filtering—was implemented to ensure data consistency and accuracy. Statistical homogeneous pixels (SHPs) were extracted to identify stable targets, and coherence-based analysis was employed to quantify temporal decorrelation and detect changes. Adaptive thresholding and morphological operations refined the detected changes, while small-segment removal mitigated noise effects. Experimental results demonstrated high reliability, with an overall accuracy of 92%, validated using confusion matrix analysis. The methodology effectively identified urban changes, highlighting the potential of KOMPSAT-5 data for post-disaster monitoring and urban change detection. Future improvements are suggested, focusing on the stability of InSAR orbits to further enhance detection precision. The findings underscore the potential for broader applications of the developed SAR time-series change detection technology, promoting increased utilization of KOMPSAT SAR data for both domestic and international research and monitoring initiatives.
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spelling doaj-art-df149a06e0e64958a2c65cf12d3a5dd12025-01-24T13:49:26ZengMDPI AGSensors1424-82202025-01-0125258310.3390/s25020583Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection AlgorithmMirza Muhammad Waqar0Heein Yang1Rahmi Sukmawati2Sung-Ho Chae3Kwan-Young Oh4Satellite Image Application Team, CONTEC, Daejeon 34074, Republic of KoreaSatellite Image Application Team, CONTEC, Daejeon 34074, Republic of KoreaSatellite Image Application Team, CONTEC, Daejeon 34074, Republic of KoreaSatellite Application Division, Korea Aerospace Research Institute (KARI), Daejeon 34133, Republic of KoreaSatellite Application Division, Korea Aerospace Research Institute (KARI), Daejeon 34133, Republic of KoreaFor change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations. Although ACD and CCD techniques can detect changes, spatial variability outside the primary target area introduces complexity into the analysis. This study presents a robust change detection methodology designed to identify urban changes using KOMPSAT-5 time-series data. A comprehensive preprocessing framework—including coregistration, radiometric terrain correction, normalization, and speckle filtering—was implemented to ensure data consistency and accuracy. Statistical homogeneous pixels (SHPs) were extracted to identify stable targets, and coherence-based analysis was employed to quantify temporal decorrelation and detect changes. Adaptive thresholding and morphological operations refined the detected changes, while small-segment removal mitigated noise effects. Experimental results demonstrated high reliability, with an overall accuracy of 92%, validated using confusion matrix analysis. The methodology effectively identified urban changes, highlighting the potential of KOMPSAT-5 data for post-disaster monitoring and urban change detection. Future improvements are suggested, focusing on the stability of InSAR orbits to further enhance detection precision. The findings underscore the potential for broader applications of the developed SAR time-series change detection technology, promoting increased utilization of KOMPSAT SAR data for both domestic and international research and monitoring initiatives.https://www.mdpi.com/1424-8220/25/2/583change detectionstatistical homogeneous pixels (SHP)KOMPSAT-5 amplitude change detection
spellingShingle Mirza Muhammad Waqar
Heein Yang
Rahmi Sukmawati
Sung-Ho Chae
Kwan-Young Oh
Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm
Sensors
change detection
statistical homogeneous pixels (SHP)
KOMPSAT-5 amplitude change detection
title Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm
title_full Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm
title_fullStr Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm
title_full_unstemmed Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm
title_short Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm
title_sort time series change detection using kompsat 5 data with statistical homogeneous pixel selection algorithm
topic change detection
statistical homogeneous pixels (SHP)
KOMPSAT-5 amplitude change detection
url https://www.mdpi.com/1424-8220/25/2/583
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