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Domain adaptation framework with ensemble of fuzzy rules-based ELMs for remote-sensing image classification

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Abstract

The domain adaptation (DA) transfer learning technique can accurately classify land cover in remote-sensing (RS) images, even with a small number of labeled samples. However, traditional architectures and various neural network and deep learning modifications make DA models difficult to learn quickly and computationally demanding. Extreme learning machine (ELM)-based DA models have shown potential for quick learning and improved generalization capabilities. Still, they are also known for their unstable and over-fitting characteristics and the ambiguity of the data sets. To address these difficulties, this article proposes two contributions. First, a modified fuzzy-rule-based ELM model (MFR-ELM) is created within a data-dependent platform to address the imprecision of the RS image land-cover classes and small sample size. Second, an ensemble of fuzzy rule-based ELM networks is proposed in the DA framework to address individual networks’ instability and weak learning properties. The MFR-ELM and other fuzzy rule-based ELM designs are combined in the ensemble architecture for mutual benefit. The suggested model is proven to be superior to competing approaches for categorizing multispectral RS images, as supported by various performance assessment indices.

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Data availability statement

The data used in the study are available in the data port: DOI:10.21227/v9v6-1197.

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Acknowledgements

The authors would like to thank the Department of Science and Technology, Government of India, under which a project titled “Granular Deep Learning Models For Remote Sensing Image Classification” (FILE NO. MTR/2022/000107) is being carried out at the Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore.

Funding

The authors received support from Department of Science and Technology, Government of India, under which a project titled “Granular Deep Learning Models For Remote Sensing Image Classification” (FILE NO. MTR/2022/000107) is being carried out at the Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore.

Author information

Author notes

  1. Neeta Sharma Kothari, Ravi Sindal, and Ganapati Pandahave contributed equally to this work.

Authors and Affiliations

  1. Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore Centre, 8th Mile, Mysore Road, RVCE Post, Bangalore, Karnataka, 560059, India

    Saroj K. Meher

  2. Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Khandwa Road, Indore, Madhya Pradesh, 452017, India

    Neeta Sharma Kothari & Ravi Sindal

  3. C. V. Raman College of Engineering, Department of Computer Science, Bhubaneswar, Odisha, 752054, India

    Ganapati Panda

Authors
  1. Saroj K. Meher
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  2. Neeta Sharma Kothari
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  3. Ravi Sindal
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  4. Ganapati Panda
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Contributions

Saroj. K. Meher: conceptualization, visualization, investigation, writing, review and editing, and supervision. Neeta S. Kothari: data curation and software implementation. Ravi Sindal and Ganapati Panda: helped perform the analysis with constructive discussions.

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Correspondence to Saroj K. Meher.

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Meher, S.K., Kothari, N.S., Sindal, R. et al. Domain adaptation framework with ensemble of fuzzy rules-based ELMs for remote-sensing image classification. Soft Comput 28, 5577–5589 (2024). https://doi.org/10.1007/s00500-023-09355-7

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