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Change detection in synthetic aperture radar images using spatial fuzzy clustering based on the similarity matrix

Tushar Zanke

Tushar Zanke

Department of Instrumentation Engineering, Vishwakarma Institute of TechnologyPune, India
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Zanke, Tushar
, 
Stuti Jagtap

Stuti Jagtap

Department of Instrumentation Engineering, Vishwakarma Institute of TechnologyPune, India
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Jagtap, Stuti
, 
Samrudhi Wath

Samrudhi Wath

Department of Instrumentation Engineering, Vishwakarma Institute of TechnologyPune, India
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Wath, Samrudhi
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Snehashish Mulgir

Snehashish Mulgir

Department of Instrumentation Engineering, Vishwakarma Institute of TechnologyPune, India
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Mulgir, Snehashish
 e 
Archana Chaudhari

Archana Chaudhari

Department of Instrumentation Engineering, Vishwakarma Institute of TechnologyPune, India
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Chaudhari, Archana
  
19 lug 2025
Change detection in synthetic aperture radar images using spatial fuzzy clustering based on the similarity matrix's Cover Image
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Categoria dell'articolo: Research Article
Pubblicato online: 19 lug 2025
Ricevuto: 07 mar 2025
DOI: https://doi.org/10.2478/ijssis-2025-0026
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© 2025 Tushar Zanke et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Illustration of CNN model architecture. CNN, convolutional neural network.
Illustration of CNN model architecture. CNN, convolutional neural network.

Figure 2:

Illustration of the average pooling operation with a 2 × 2 filter and stride.
Illustration of the average pooling operation with a 2 × 2 filter and stride.

Figure 3:

Flowchart of the proposed method. CNN, convolutional neural network; SFCM, spatial fuzzy clustering membership.
Flowchart of the proposed method. CNN, convolutional neural network; SFCM, spatial fuzzy clustering membership.

Figure 4:

Output of model training.
Output of model training.

Figure 5:

SAR1 image before change.
SAR1 image before change.

Figure 6:

SAR2 image after change.
SAR2 image after change.

Figure 7:

Pre-classification of SAR1 & SAR2 using space matrix calculation and SFCM. SFCM, spatial fuzzy clustering membership.
Pre-classification of SAR1 & SAR2 using space matrix calculation and SFCM. SFCM, spatial fuzzy clustering membership.

Figure 8:

Final change detection output.
Final change detection output.

Comparison with state-of-the-art methods

Methods Accuracy (%)
Proposed method 98.53
FCM [20] 91.29
PCAKM [20] 90.45
MRFFCM [20] 91.27
NR-ELM [21] 88.93
DBN [21] 87.22
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Inglese
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