Deep learning driven content-based image time-series retrieval in remote sensing archives
| dc.contributor.author | Vuran, Onat | |
| dc.contributor.author | Akcin, Oguzhan | |
| dc.contributor.author | Ravanbakhsh, Mahdyar | |
| dc.contributor.author | Sankur, Bülent | |
| dc.contributor.author | Demir, Begüm | |
| dc.date.accessioned | 2022-11-15T11:46:11Z | |
| dc.date.available | 2022-11-15T11:46:11Z | |
| dc.date.issued | 2022-09-28 | |
| dc.description.abstract | The rapid evolution of satellite imaging systems has resulted in sharp increases of image archive volumes. Multitemporal images constitute a sizeable portion of these time-series databases. Accordingly, development of accurate content based time-series retrieval (CBTSR) methods in massive archives of RS images attracts much research interest. Given a user-defined query time series, CBTSR aims at identifying within a massive archive image time series that show characteristics similar to those of the query time series. In this paper, we focus our attention to CBTSR in pairs of RS images, aiming to search and retrieve bi-temporal image pairs containing changes similar to those modeled in the query. To this end, we introduce two deep learning-based methods in the framework of CBTSR. The first method, called deep change vector retrieval (DVCR), is based on selected deep features extracted from the change vector analysis. The second method, called autoencoder with early fusion (AEEF) uses an autoencoder architecture to recreate the time difference images and the latent codes produced by this network. Experimental results show the effectiveness of the proposed methods for CBTSR problems. The code of the proposed methods is available at: https://github.com/OnatV/ChangeRetrieval. | en |
| dc.description.sponsorship | EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarth | |
| dc.identifier.eissn | 2153-7003 | |
| dc.identifier.isbn | 978-1-6654-2792-0 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/17669 | |
| dc.identifier.uri | https://doi.org/10.14279/depositonce-16454 | |
| dc.language.iso | en | |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.subject.ddc | 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten | de |
| dc.subject.other | content based time-series retrieval | en |
| dc.subject.other | deep learning | en |
| dc.subject.other | remote sensing | en |
| dc.subject.other | learning systems | en |
| dc.subject.other | satellites | en |
| dc.title | Deep learning driven content-based image time-series retrieval in remote sensing archives | en |
| dc.type | Conference Object | |
| dc.type.version | acceptedVersion | |
| dcterms.bibliographicCitation.articlenumber | 22091027 | |
| dcterms.bibliographicCitation.doi | 10.1109/IGARSS46834.2022.9884495 | |
| dcterms.bibliographicCitation.originalpublishername | IEEE | |
| dcterms.bibliographicCitation.originalpublisherplace | London | |
| dcterms.bibliographicCitation.proceedingstitle | IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium | |
| tub.accessrights.dnb | free | |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Technische Informatik und Mikroelektronik::FG Remote Sensing Image Analysis Group | |
| tub.publisher.universityorinstitution | Technische Universität Berlin |