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APO-AN feature selection based Glorot Init Optimal TransCNN landslide detection from multi source satellite imagery

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Abstract

The work presents a comprehensive approach for landslide detection in satellite imagery by combining multiple techniques. The proposed methodology involves denoising the landslide image using the maximum information coefficient-based Wavelet Filter (MIC-WF) to enhance image quality. The denoising process with MIC-WF effectively reduces noise in the landslide image, resulting in improved image clarity and quality. To address the issue of overlapping pixels, an innovative approach based on Independent Component Analysis (ICA) based “Density-Based Spatial Clustering of Applications with Noise (DBSCAN)” is exploited for pixel separation. The separation of overlapping pixels using ICA-DBSCAN overcomes the challenge of pixel ambiguity, allowing for precise identification of individual landslide areas. Furthermore, the feature point discriptor as a feature selection method based on the Archimedes principle optimization-based attention network (APO-AN) is utilized to identify the most informative features for landslide detection. The feature selection based on APO-AN ensures that only the most relevant features are used for landslide detection, reducing computational complexity and enhancing model efficiency. Finally, a landslide detection model is developed using the Glorot Initialization Optimal TransCNN approach for accurate classification of landslide areas, leveraging the benefits of optimized weight initialization. FMRCNN, R-CNN, M-CNN, and CNN techniques achieve specificities of 93.47%, 92.11%, 91.78%, and 90.25%, respectively. The denoising, pixel separation, feature selection, and classification stages collectively contribute to the accurate detection of landslide areas in satellite imagery. The integration of these techniques offers a comprehensive solution for landslide detection, aiding in early warning systems and disaster management.

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Funding

The authors declare they have no financial interests.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Patna, Dayalpur Daulatpur, 801106, India

    Abhijit Kumar, Rajiv Misra & T. N. Singh

  2. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 248007, India

    Abhijit Kumar

  3. Department of Earth Science, Indian Institute of Technology Bombay, Mumbai, 400076, India

    T. N. Singh

  4. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Gaurav Dhiman

  5. Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Gharuan, 140413, Mohali, India

    Gaurav Dhiman

  6. Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun, 248002, India

    Gaurav Dhiman

  7. Division of Research and Development, Lovely Professional University, Phagwara, India

    Gaurav Dhiman

  8. School of Sciences and Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Patiala, 147001, India

    Gaurav Dhiman

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  1. Abhijit Kumar
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  2. Rajiv Misra
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Contributions

Abhijit Kumar: contributed to the study’s conception and design, manuscript writing, data collection and data analysis.

Rajiv Misra: contributed to the study’s conception, manuscript review, proof reading and over all supervision of the project.

T N Singh: contributed to problem statement formulation and over all supervision.

Gaurav Dhiman supervised the work.

All authors read and approved the final manuscript.

Corresponding author

Correspondence to Gaurav Dhiman.

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This work is supported by Visvesvaraya PhD Scheme, MeitY, Govt. of India MEITY-PHD-2527.

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Kumar, A., Misra, R., Singh, T.N. et al. APO-AN feature selection based Glorot Init Optimal TransCNN landslide detection from multi source satellite imagery. Multimed Tools Appl 83, 40451–40488 (2024). https://doi.org/10.1007/s11042-023-17090-2

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  • DOI: https://doi.org/10.1007/s11042-023-17090-2

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