Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Sumbul, Gencer"'
Autor:
Clasen, Kai Norman, Hackel, Leonard, Burgert, Tom, Sumbul, Gencer, Demir, Begüm, Markl, Volker
This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote sensing dataset constructed to support deep learning (DL) studies for remote sensing image analysis. The reBEN dataset consists of 549,488 pairs of Sentinel-1 a
Externí odkaz:
http://arxiv.org/abs/2407.03653
Deep metric learning (DML) has shown to be effective for content-based image retrieval (CBIR) in remote sensing (RS). Most of DML methods for CBIR rely on a high number of annotated images to accurately learn model parameters of deep neural networks
Externí odkaz:
http://arxiv.org/abs/2406.10107
Self-supervised learning through masked autoencoders (MAEs) has recently attracted great attention for remote sensing (RS) image representation learning, and thus embodies a significant potential for content-based image retrieval (CBIR) from ever-gro
Externí odkaz:
http://arxiv.org/abs/2401.07782
Federated learning (FL) enables the collaboration of multiple deep learning models to learn from decentralized data archives (i.e., clients) without accessing data on clients. Although FL offers ample opportunities in knowledge discovery from distrib
Externí odkaz:
http://arxiv.org/abs/2311.06141
Deep metric learning (DML) based methods have been found very effective for content-based image retrieval (CBIR) in remote sensing (RS). For accurately learning the model parameters of deep neural networks, most of the DML methods require a high numb
Externí odkaz:
http://arxiv.org/abs/2306.11605
Autor:
Sumbul, Gencer, Demir, Begüm
Due to the publicly available thematic maps and crowd-sourced data, remote sensing (RS) image annotations can be gathered at zero cost for training deep neural networks (DNNs). However, such annotation sources may increase the risk of including noisy
Externí odkaz:
http://arxiv.org/abs/2306.08575
The development of federated learning (FL) methods, which aim to learn from distributed databases (i.e., clients) without accessing data on clients, has recently attracted great attention. Most of these methods assume that the clients are associated
Externí odkaz:
http://arxiv.org/abs/2306.00792
Autor:
Sumbul, Gencer, Demir, Begüm
The development of deep learning based image representation learning (IRL) methods has attracted great attention for various image understanding problems. Most of these methods require the availability of a high quantity and quality of annotated trai
Externí odkaz:
http://arxiv.org/abs/2212.01261
In this letter, we introduce deep active learning (AL) for multi-label classification (MLC) problems in remote sensing (RS). In particular, we investigate the effectiveness of several AL query functions for MLC of RS images. Unlike the existing AL qu
Externí odkaz:
http://arxiv.org/abs/2212.01165
Due to the availability of multi-modal remote sensing (RS) image archives, one of the most important research topics is the development of cross-modal RS image retrieval (CM-RSIR) methods that search semantically similar images across different modal
Externí odkaz:
http://arxiv.org/abs/2202.11429