TEGOA-CNN: An Improved Gannet Optimization Algorithm for CNN Hyperparameter Optimization in Remote Sensing Sence Classification

The evolution of remote sensing technology has led to significant improvements in high-resolution and hyperspectral image acquisition, enhancing applications like environmental monitoring and disaster assessment. However, the high dimensionality, nonlinearity, and heterogeneity of these images pose...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 17; číslo 17; s. 3087
Hlavní autoři: Wu, Tsu-Yang, Yu, Chengyuan, Li, Haonan, Kumari, Saru, Por, Lip Yee
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2025
Témata:
Accuracy
Algorithms
Artificial neural networks
Classification
Datasets
Deep learning
elite retention
Environmental monitoring
Feature selection
Gannet Optimization Algorithm
Genetic algorithms
Heterogeneity
Heuristic methods
hyperparameters optimization
Hyperspectral imaging
Image acquisition
Image resolution
Integrated approach
Land use
Machine learning
Mathematical optimization
Mutation
Neural networks
Nonlinear systems
Optimization
Optimization algorithms
Parameters
Population decline
Remote sensing
remote sensing sence classification
Support vector machines
T-distribution perturbation
ISSN:2072-4292, 2072-4292
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Shrnutí:The evolution of remote sensing technology has led to significant improvements in high-resolution and hyperspectral image acquisition, enhancing applications like environmental monitoring and disaster assessment. However, the high dimensionality, nonlinearity, and heterogeneity of these images pose challenges for intelligent interpretation. While deep learning models (e.g., CNN) require balancing efficiency and parameter optimization, meta-heuristic algorithms establish self-organizing, parallelized search mechanisms capable of achieving asymptotic approximation towards the global optimum of parameters without requiring gradient information. In this paper, we first propose an improved Gannet Optimization Algorithm (GOA), named TEGOA, which uses the T-distribution perturbation and elite retention to address CNN’s parameter dependency. The experiment on CEC2017 shows that TEGOA has a better performance on composition functions. Hence, it is suitable for solving complex optimization problems. Then, we propose a classification model TEGOA-CNN, which combines TEGOA with CNN to increase the accuracy and efficiency of remote sensing sence classification. The experiments of TEGOA-CNN on two well-known datasets, UCM and AID, showed a higher performance in classification accuracy of remote sensing images. Particularly, TEGOA-CNN achieves 100% classification accuracy on 10 out of the 21 surface categories of UCM.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs17173087