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dc.contributor.authorGrahn, Jakob
dc.contributor.authorBianchi, Filippo Maria
dc.date.accessioned2022-11-07T10:11:53Z
dc.date.available2022-11-07T10:11:53Z
dc.date.issued2022-09-06
dc.description.abstractIn this article, we explore the possibility of detecting polar lows in C-band synthetic aperture radar (SAR) images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence and absence of a maritime mesocyclone, respectively. The dataset is constructed using the ECMWF reanalysis version 5 (ERA5) dataset as baseline and it consists of 2004 annotated images. To our knowledge, this is the first dataset of its kind to be publicly released. The dataset is used to train a deep learning model to classify the labeled images. Evaluated on an independent test set, the model yields an F1 score of 0.95, indicating that polar lows can be consistently detected from SAR images. Interpretability techniques applied to the deep learning model reveal that atmospheric fronts and cyclonic eyes are key features in the classification. Moreover, experimental results show that the model is accurate even if: 1) such features are significantly cropped due to the limited swath width of the SAR; 2) the features are partly covered by sea ice; and 3) land is covering significant parts of the images. By evaluating the model performance on multiple input image resolutions (pixel sizes of 500 m, 1 km, and 2 km), it is found that higher resolution yield the best performance. This emphasizes the potential of using high-resolution sensors like SAR for detecting polar lows, as compared to conventionally used sensors such as scatterometers.en_US
dc.identifier.citationGrahn, Bianchi. Recognition of polar lows in Sentinel-1 SAR images with deep learning. IEEE Transactions on Geoscience and Remote Sensing. 2022en_US
dc.identifier.cristinIDFRIDAID 2069615
dc.identifier.doi10.1109/tgrs.2022.3204886
dc.identifier.issn0196-2892
dc.identifier.issn1558-0644
dc.identifier.urihttps://hdl.handle.net/10037/27275
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE Transactions on Geoscience and Remote Sensing
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titleRecognition of polar lows in Sentinel-1 SAR images with deep learningen_US
dc.type.versionsubmittedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US


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