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

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

International Journal on Smart Sensing and Intelligent Systems
Volume 18 (2025): Issue 1 (January 2025)
By: Tushar Zanke,  Stuti Jagtap,  Samrudhi Wath,  Snehashish Mulgir and  Archana Chaudhari  
Open Access
|Jul 2025

Figures & Tables

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

MethodsAccuracy (%)
Proposed method98.53
FCM [20]91.29
PCAKM [20]90.45
MRFFCM [20]91.27
NR-ELM [21]88.93
DBN [21]87.22
DOI: https://doi.org/10.2478/ijssis-2025-0026 | Journal eISSN: 1178-5608
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Language: English
Submitted on: Mar 7, 2025
|
Published on: Jul 19, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Deep Learning,
Synthetic Aperture Radar,
Convolutional Neural Networks,
Pattern Recognition,
Noise Robustness,
SAR Imaging Applications
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2025 Tushar Zanke, Stuti Jagtap, Samrudhi Wath, Snehashish Mulgir, Archana Chaudhari, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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