Using a dynamic arithmetic optimization approach to improve ridgelet neural network performance in remote sensing scene classification

This research proposes an innovative methodology for acurate remore sensing scene classification. Here, a new design of dynamic arithmetic optimization algorithm (DAOA) has been proposed to enhance the performance of a Ridgelet neural network (RNN) in this purpose. The RNN is commonly used in image...

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Vydané v:Scientific reports
Hlavní autori: Zhang, Hui, Chen, Jun, Xie, Hui, Bagi, Kambiz
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 21.11.2025
Predmet:
Deep learning
Computer vision
Image processing
Ridgelet neural network
Dynamic arithmetic optimization algorithm
Optimization techniques
Hyperparameter optimization
Remote sensing scene classification
ISSN:2045-2322, 2045-2322
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Shrnutí:This research proposes an innovative methodology for acurate remore sensing scene classification. Here, a new design of dynamic arithmetic optimization algorithm (DAOA) has been proposed to enhance the performance of a Ridgelet neural network (RNN) in this purpose. The RNN is commonly used in image processing and computer vision, but its effectiveness can be hindered by subpar hyperparameter selection. To tackle this problem, the utilization of DAOA has been proposed as a robust optimization technique to automatically search for optimal hyperparameters in the RNN model. The proposed method has been assessed on UC Merced Land Use publicly available dataset frequently employed in remote sensing scene classification. The experimental results show that the proposed approach significantly enhances the efficiency of the RNN when compared to other cutting-edge methods. The findings indicate that the combination of optimization algorithms like DAOA with deep learning models such as RNNs has the potential to yield more precise and efficient solutions for remote sensing scene classification tasks.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-28490-9