Weather classification with deep convolutional neural networks

Mohamed Elhoseiny, Sheng Huang, Ahmed Elgammal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

88 Scopus citations


In this paper, we study weather classification from images using Convolutional Neural Networks (CNNs). Our approach outperforms the state of the art by a huge margin in the weather classification task. Our approach achieves 82.2% normalized classification accuracy instead of 53.1% for the state of the art (i.e., 54.8% relative improvement). We also studied the behavior of all the layers of the Convolutional Neural Networks, we adopted, and interesting findings are discussed.
Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Image Processing, ICIP
PublisherIEEE Computer
ISBN (Print)9781479983391
StatePublished - Dec 9 2015
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2019-11-20


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