Metaheuristic Optimization of Deep Learning Models for Land Cover Classification Using Remote Sensing Data

Title

Metaheuristic Optimization of Deep Learning Models for Land Cover Classification Using Remote Sensing Data

Creator

Antony, Anil; Ganesh Kumar, R.

Description

Deep learning techniques have greatly advanced land-cover classification from remote sensing imagery, but their performance depends critically on choosing optimal hyperparameters. Manually tuning hyperparameters (e.g., learning rate, network depth, dropout rate) is time-consuming and often suboptimal. Metaheuristic algorithms offer an automated approach to this problem. In this work, we compare five metaheuristic optimizersParticle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), African Vulture Optimization Algorithm (AVOA), and an Enhanced Dipper Throat Optimization Algorithm (EDTOA)for hyperparameter tuning of convolutional neural networks (CNNs), a ResNet-50, and a U-Net. We evaluate these methods on two benchmark land-cover datasets: EuroSAT (patch-level multispectral image classification) and DeepGlobe (pixel-wise satellite image segmentation). Our data preprocessing includes normalization, data augmentation, and computing spectral indices (e.g., NDVI) to enrich the feature set. Each metaheuristic searches the hyperparameter space to maximize validation accuracy (for EuroSAT) or mean Intersection-over Union (mIoU) (for DeepGlobe). In addition to predictive performance, we analyze the computational cost (wall-clock time, epochs to convergence, GPU usage) of each optimizer to assess the trade-off between efficiency and accuracy. AVOA and EDTOA achieve the best results on both datasets (e.g., up to 98.5% accuracy on EuroSAT and 56% mIoU on DeepGlobe), outperforming the PSO, GA, and DE baselines while offering favorable cost-performance balance. These findings demonstrate that advanced metaheuristics can significantly improve deep model performance in land-cover classification. Our contributions include a comprehensive experimental comparison of five optimizers, a detailed methodology integrating spectral index features, a cost performance analysis, and reference results to guide future research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Source

Lecture Notes in Networks and Systems;Volume;1826 LNNS;pp.25-34

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

CNN; deep learning; hyperparameter tuning; land cover classification; Metaheuristic optimization; remote sensing; ResNet,; U-Net

Coverage

Antony A., CHRIST (Deemed to be University), School of Engineering and Technology Kengeri Campus, Bangalore, India, Department of Computer Science and Engineering, Sahrdaya College of Engineering and Technology, Kerala, Thrissur, India; Ganesh Kumar R., CHRIST (Deemed to be University), School of Engineering and Technology Kengeri Campus, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-303218134-3;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-18135-0_3

https://www.scopus.com/pages/publications/105038861732?origin=resultslist