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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21.11.2025
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| Abstract | 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. |
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| AbstractList | 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.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. 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. |
| Author | Chen, Jun Zhang, Hui Bagi, Kambiz Xie, Hui |
| Author_xml | – sequence: 1 givenname: Hui surname: Zhang fullname: Zhang, Hui – sequence: 2 givenname: Jun surname: Chen fullname: Chen, Jun – sequence: 3 givenname: Hui surname: Xie fullname: Xie, Hui – sequence: 4 givenname: Kambiz surname: Bagi fullname: Bagi, Kambiz |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41271988$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3390/s20071999 10.1145/1869790.1869829 10.1109/ISCBI.2015.8 10.1016/j.bspc.2025.108582 10.1038/s41598-025-87240-z 10.1016/j.knosys.2018.06.001 10.1109/JSTARS.2020.3005403 10.1049/PBCE120F_ch2 10.3390/s20143906 10.1049/iet-rpg.2019.0485 10.1016/j.cma.2020.113609 10.1098/rsta.1999.0444 10.1016/j.inffus.2023.102192 10.3390/rs12203292 10.1109/JSTARS.2020.3011333 10.1109/LGRS.2025.3576662 10.1109/JSTARS.2020.3018307 10.1109/JSTARS.2022.3141826 10.1109/TEVC.2008.919004 10.3390/rs14061478 10.3390/rs14092042 10.3233/JIFS-190495 |
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| Keywords | Deep learning Computer vision Image processing Ridgelet neural network Dynamic arithmetic optimization algorithm Optimization techniques Hyperparameter optimization Remote sensing scene classification |
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| References | S Akodad (28490_CR14) 2020; 12 W Chen (28490_CR8) 2022; 15 L Abualigah (28490_CR27) 2021; 376 28490_CR9 D Simon (28490_CR29) 2008; 12 EJ Candès (28490_CR26) 1999; 357 B Tu (28490_CR11) 2024; 104 D Yu (28490_CR4) 2019; 13 W Zehao (28490_CR21) 2025; 74 G Zhou (28490_CR7) 2022; 60 X Tang (28490_CR10) 2022; 60 F Peng (28490_CR23) 2022; 14 28490_CR31 28490_CR6 C Gong (28490_CR18) 2026; 112 G Dhiman (28490_CR30) 2018; 159 28490_CR12 G Cheng (28490_CR3) 2020; 13 G Zhou (28490_CR5) 2024; 62 28490_CR32 28490_CR15 Z Cui (28490_CR28) 2019; 62 W Wang (28490_CR22) 2022; 60 E Li (28490_CR13) 2020; 19 B Petrovska (28490_CR17) 2020; 20 J Liang (28490_CR2) 2020; 13 28490_CR24 D Yu (28490_CR16) 2020; 20 G Zhou (28490_CR1) 2023; 61 C Shi (28490_CR19) 2020; 13 G Chen (28490_CR20) 2025; 15 28490_CR25 |
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