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An accurate algorithm for land surface changes detection based on deep learning and improved pixel clustering using SAR images

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Abstract

Synthetic Aperture Radar (SAR) was developed to map the terrain without the use of large antennas. Combined array is one of the radar methods that is applied from an aircraft or a space platform and in which an effective aperture of the antenna is created in a combined manner. The images obtained by these radars are very accurate. On the other hand, the surface of the earth always changes due to various factors. Accurate identification of these changes can be used in many applications, especially in Iraq due to its semidesert structure. In this research, an improved surface change detection algorithm based on morphological transformation, two-stage center-constrained FCM algorithm (TCCFCM) clustering and deep learning is presented. The simulation of the method of this research on MATLAB software shows that the proposed method is fast and inexpensive. The accuracy was 99.7%.

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Availability of data and materials

The most datasets generated and/or analysed in this study are comprised in this submitted manuscript. The other datasets are available on reasonable request from the corresponding author with the attached information.

Code availability

The code used for analysis in this study is available on reasonable request from the corresponding author with the attached information.

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Funding

This research was not supported by any Funding Agency.

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Authors and Affiliations

  1. Department of Electronic and Communication, Faculty of Engineering, University of Kufa, Najaf, Iraq

    Mohammed Jawad Al-Dujaili

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  1. Mohammed Jawad Al-Dujaili
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Contributions

MJA-D conceived and designed the study performed experiments and collected data analyzed and interpreted the data wrote the manuscript. reviewed and approved the final version.

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Correspondence to Mohammed Jawad Al-Dujaili.

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Al-Dujaili, M.J. An accurate algorithm for land surface changes detection based on deep learning and improved pixel clustering using SAR images. Neural Comput & Applic 36, 5545–5554 (2024). https://doi.org/10.1007/s00521-023-09377-0

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  • DOI: https://doi.org/10.1007/s00521-023-09377-0

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