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Detection of changes in synthetic aperture radar images using Modified Gauss-Log ratio and Fuzzy Local Information C-Means clustering

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Abstract

Change detection in SAR is currently known to be a happening field of research in the domain of computer vision and remote sensing. There are numerous approaches and techniques available to detect the change in varied types of images captured in various areas. In this paper, we present a technique for detecting changes in SAR images, which generally happen to be poor contrast and poor brightness grayscale images. Consequently, they are complicated to change detection. We developed a change detection strategy in this study that employs a Convolution Neural Network (CNN) as a classification model. Further, we have used the Fuzzy Local Information C-Means approach to find out interesting pixels which have a high possibility of being changed or unchanged. By integrating the CNN classification result with the pre-classification result, the final change map is created. In this paper, we have tried to generate some virtual samples to supplement the lack of training samples. The efficiency and robustness of the planned approach have been ascertained with investigational results on four real SAR image data sets compared to several existing methodologies.

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Authors and Affiliations

  1. Department of MCA, Calcutta Institute of Technology, Banitabla, Uluberia, Howrah, 711316, India

    Chanchal Ghosh

  2. Department of CSE, RCC Institute of Information Technology, Kolkata 15, India

    Dipankar Majumdar

  3. Department of CSE, B.P. Poddar Institute of Management and Technology, Kolkata 52, India

    Bikromadittya Mondal

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  1. Chanchal Ghosh
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  2. Dipankar Majumdar
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  3. Bikromadittya Mondal
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Correspondence to Chanchal Ghosh.

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Ghosh, C., Majumdar, D. & Mondal, B. Detection of changes in synthetic aperture radar images using Modified Gauss-Log ratio and Fuzzy Local Information C-Means clustering. Multimed Tools Appl 82, 42661–42678 (2023). https://doi.org/10.1007/s11042-023-15187-2

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  • DOI: https://doi.org/10.1007/s11042-023-15187-2

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