Enhanced weather classification using xception with SENet and attention mechanisms

Shandilya, Gunjan and Gupta, Sheifali and Saudagar, Abdul Khader Jilani and Ikram, Sunnia and Rehman, Ateeq Ur and De la Torre Díez, Isabel and Mohamed, Heba G. and Pali-Casanova, Ramón and Kuc Castilla, Ángel Gabriel and Kaur, Upinder UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, ramon.pali@unini.edu.mx, angel.kuc@uneatlantico.es, UNSPECIFIED (2026) Enhanced weather classification using xception with SENet and attention mechanisms. Frontiers in Environmental Science, 14. ISSN 2296-665X

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Abstract

Introduction: Weather classification plays a crucial role in applications such as environmental monitoring, disaster management, and smart city infrastructure. Accurate and efficient classification of weather conditions from images remains a challenging task due to variations in illumination, texture, and atmospheric conditions.Methods: This study proposes an efficient deep learning framework for multi-class weather classification by integrating the Xception architecture with Squeeze-and-Excitation (SE) blocks and a spatial attention mechanism. Transfer learning with pre-trained ImageNet weights was employed, and a comparative analysis was conducted using EfficientNet-B3, ResNet152V2, and Xception architectures. The proposed enhanced Xception model incorporates channel-wise recalibration and spatial feature refinement to improve representational capability. The model was trained and evaluated on the Multi-Class Weather Dataset (MWD), which consists of 1,125 images categorized into four classes: sunshine, cloudy, rain, and sunrise. To ensure robustness and generalization, 5-fold cross-validation, statistical significance testing, calibration analysis, and robustness evaluation under image perturbations were performed.Results: The proposed model achieved a classification accuracy of 99.06% on the test set. Additionally, it attained a macro precision of 98.3%, macro recall of 97.7%, and macro F1-score of 98.0%. The model demonstrated strong generalization capability and robustness under varying perturbation conditions, with only moderate computational overhead.Discussion: The integration of SE blocks and spatial attention significantly enhances feature representation by emphasizing informative channels and spatial regions. Compared to baseline architectures, the proposed framework shows superior performance in terms of accuracy and robustness. These results indicate that the model is well-suited for real-world weather classification applications, particularly in intelligent environmental monitoring systems.

Item Type:	Article
Uncontrolled Keywords:	attention mechanisms, cloudy, deep learning (DL), image classification, rain, squeeze and excitation (SE), sunrise, sunshine
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Articles and books Fundación Universitaria Internacional de Colombia > Research > Articles and books Ibero-american International University > Research > Articles and Books Universidad Internacional do Cuanza > Research > Articles and books University of La Romana > Research > Scientific Production
Depositing User:	Sr Bibliotecario
Date Deposited:	06 Jul 2026 08:48
Last Modified:	06 Jul 2026 08:48
URI:	https://repositorio.funiber.org/id/eprint/28590

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