Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding
| dc.contributor.author | Sumbul, Gencer | |
| dc.contributor.author | Charfuelan, Marcela | |
| dc.contributor.author | Demir, Begüm | |
| dc.contributor.author | Markl, Volker | |
| dc.date.accessioned | 2019-11-25T20:31:27Z | |
| dc.date.available | 2019-11-25T20:31:27Z | |
| dc.date.issued | 2019-11-14 | |
| dc.description | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
| dc.description.abstract | This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive. The BigEarthNet consists of 590, 326 Sentinel-2 image patches, each of which is a section of i) 120 × 120 pixels for 10m bands; ii) 60×60 pixels for 20m bands; and iii) 20×20 pixels for 60m bands. Unlike most of the existing archives, each image patch is annotated by multiple land-cover classes (i.e., multi-labels) that are provided from the CORINE Land Cover database of the year 2018 (CLC 2018). The BigEarthNet is significantly larger than the existing archives in remote sensing (RS) and thus is much more convenient to be used as a training source in the context of deep learning. This paper first addresses the limitations of the existing archives and then describes the properties of the BigEarthNet. Experimental results obtained in the framework of RS image scene classification problems show that a shallow Convolutional Neural Network (CNN) architecture trained on the BigEarthNet provides much higher accuracy compared to a state-of-the-art CNN model pre-trained on the ImageNet (which is a very popular large-scale benchmark archive in computer vision). The BigEarthNet opens up promising directions to advance operational RS applications and research in massive Sentinel-2 image archives. | en |
| dc.description.sponsorship | EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarth | en |
| dc.description.sponsorship | BMBF, 01IS14013A, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big Data | en |
| dc.identifier.eissn | 2153-7003 | |
| dc.identifier.isbn | 978-1-5386-9154-0 | |
| dc.identifier.isbn | 978-1-5386-9155-7 | |
| dc.identifier.issn | 2153-6996 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/10386 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-9346 | |
| dc.language.iso | en | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject.ddc | 006 Spezielle Computerverfahren | de |
| dc.subject.other | Sentinel-2 image archive | en |
| dc.subject.other | multi-label image classification | en |
| dc.subject.other | deep neural network | en |
| dc.subject.other | remote sensing | en |
| dc.title | Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding | en |
| dc.type | Conference Object | en |
| dc.type.version | acceptedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1109/IGARSS.2019.8900532 | en |
| dcterms.bibliographicCitation.originalpublishername | Institute of Electrical and Electronics Engineers (IEEE) | en |
| dcterms.bibliographicCitation.originalpublisherplace | New York, NY | en |
| dcterms.bibliographicCitation.pageend | 5904 | en |
| dcterms.bibliographicCitation.pagestart | 5901 | en |
| dcterms.bibliographicCitation.proceedingstitle | IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Technische Informatik und Mikroelektronik::FG Remote Sensing Image Analysis Group | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Remote Sensing Image Analysis Group | de |
| tub.affiliation.institute | Inst. Technische Informatik und Mikroelektronik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |