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primary topic: Enhanced weather classification using xception with SENet and attention mechanisms

Enhanced weather classification using xception with SENet and attention mechanisms
type: AcademicArticle
type: Article
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 intell…
date: 2026-05
volume: 14
is part of: Frontiers in Environmental Science
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Enhanced weather classification using xception with SENet and attention mechanisms (Text)
has document: Enhanced weather classification using xception with SENet and attention mechanisms (Other)
subject: Engineering

Enhanced weather classification using xception with SENet and attention mechanisms (Text)
type: Document
has file: fenvs-14-1797545.pdf
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Frontiers in Environmental Science
type: Collection
issn: 2296665X

Ángel Gabriel Kuc Castilla
type: Person
given name: Ángel Gabriel
family name: Kuc Castilla

Heba G. Mohamed
type: Person
given name: Heba G.
family name: Mohamed

Upinder Kaur
type: Person
given name: Upinder
family name: Kaur

Sheifali Gupta
type: Person
given name: Sheifali
family name: Gupta

Ramón Pali-Casanova
type: Person
given name: Ramón
family name: Pali-Casanova

Ateeq Ur Rehman
type: Person
given name: Ateeq Ur
family name: Rehman

Gunjan Shandilya
type: Person
given name: Gunjan
family name: Shandilya

Abdul Khader Jilani Saudagar
type: Person
given name: Abdul Khader Jilani
family name: Saudagar

Sunnia Ikram
type: Person
given name: Sunnia
family name: Ikram

Isabel De la Torre Díez
type: Person
given name: Isabel
family name: De la Torre Díez

Enhanced weather classification using xception with SENet and attention mechanisms (Other)
type: Document
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primary topic: Enhanced weather classification using xception with SENet and attention mechanisms

Engineering
type: Concept
preferred label: Ingeniería @es