BigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding

This paper presents BigEarthNet that is a large-scale Sentinel-2 multispectral image dataset with a new class nomenclature to advance deep learning (DL) studies in remote sensing (RS). BigEarthNet is made up of 590,326 image patches annotated with multi-labels provided by the CORINE Land Cover (CLC)...

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Bibliographic Details
Published inarXiv.org
Main Authors Gencer Sumbul, Kang, Jian, Kreuziger, Tristan, Filipe Marcelino, Costa, Hugo, Benevides, Pedro, Caetano, Mario, Demir, Begüm
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 13.06.2021
Subjects
Algorithms
Deep learning
Environment models
Image analysis
Image classification
Image management
Image retrieval
Land cover
Machine learning
Neural networks
Remote sensing
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Summary:This paper presents BigEarthNet that is a large-scale Sentinel-2 multispectral image dataset with a new class nomenclature to advance deep learning (DL) studies in remote sensing (RS). BigEarthNet is made up of 590,326 image patches annotated with multi-labels provided by the CORINE Land Cover (CLC) map of 2018 based on its most thematic detailed Level-3 class nomenclature. Initial research demonstrates that some CLC classes are challenging to be accurately described by considering only Sentinel-2 images. To increase the effectiveness of BigEarthNet, in this paper we introduce an alternative class-nomenclature to allow DL models for better learning and describing the complex spatial and spectral information content of the Sentinel-2 images. This is achieved by interpreting and arranging the CLC Level-3 nomenclature based on the properties of Sentinel-2 images in a new nomenclature of 19 classes. Then, the new class-nomenclature of BigEarthNet is used within state-of-the-art DL models in the context of multi-label classification. Results show that the models trained from scratch on BigEarthNet outperform those pre-trained on ImageNet, especially in relation to some complex classes including agriculture, other vegetated and natural environments. All DL models are made publicly available at http://bigearth.net/#downloads, offering an important resource to guide future progress on RS image analysis.
ISSN:2331-8422