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

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 quickl...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2024-03, Vol.28 (6), p.5577-5589
Main Authors: Meher, Saroj K., Kothari, Neeta Sharma, Sindal, Ravi, Panda, Ganapati
Format: Article
Language:English
Subjects:
Adaptation
Application of Soft Computing
Artificial Intelligence
Artificial neural networks
Classification
Computational Intelligence
Control
Deep learning
Engineering
Image classification
Land cover
Machine learning
Mathematical Logic and Foundations
Mechatronics
Neural networks
Performance assessment
Remote sensing
Robotics
Semantics
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Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-09355-7