Multitemporal Images Change Detection Using Nonsubsampled Contourlet Transform and Kernel Fuzzy C-Means Clustering

2011 ◽  
Author(s):  
Chao Wu ◽  
Yiquan Wu
Keyword(s):  
Change Detection ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals Performance Evaluation of Change Detection in SAR Images Based on Hybrid Antlion DWT Fuzzy c-Means Clustering

2021 ◽  
Vol 21 (2) ◽  
pp. 45-57
Author(s):  
J. Thrisul Kumar ◽  
B. M. S. Rani ◽  
M. Satish Kumar ◽  
M. V. Raju ◽  
K. Maria Das
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Geographical Area ◽  
Ground Truth ◽  
Sar Images ◽  
Fuzzy C Means ◽  
Difference Image ◽  
Multi Temporal ◽  
Fuzzy C Means Clustering ◽  
Log Ratio

Abstract In this paper, the main objective is to detect changes in the geographical area of Ottawa city in Canada due to floods. Two multi-temporal Synthetic Aperture Radar (SAR) images have been taken to evaluate the un-supervised change detection process. In this process, two ratio operators named as Log-Ratio and Mean-Ratio are used to generate a difference image. Performing image fusion based on DWT by selecting optimum filter coefficients by satisfying the wavelet filter coefficient properties through a novel image fusion technique is named as ADWT. GA, PSO, AntLion Optimization algorithms (ALO) and Hybridized AntLion Algorithm (HALO) have been adapted to perform the ADWT based image fusion. Segmentation has been performed based on fuzzy c-Means clustering to detect changed and unchanged pixels. Finally, the performance of the proposed method will be analysed by comparing the segmented image with the ground truth image in terms of sensitivity, accuracy, specificity, precision, F1-score.

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