548 citations · 1.2k across the 34 of their papers we have counts for
34 papers
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification
Barış Büyüktaş, Gencer Sumbul, Begüm Demir
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. Alth…
Ben-ge: Extending BigEarthNet with Geographical and Environmental Data
Michael Mommert, Nicolas Kesseli, Joëlle Hanna +3
Deep learning methods have proven to be a powerful tool in the analysis of large amounts of complex Earth observation data. However, while Earth observation data are multi-modal in…
Annotation Cost Efficient Active Learning for Content Based Image Retrieval
Julia Henkel, Genc Hoxha, Gencer Sumbul +2
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 parameter…
Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images
Lars Möllenbrok, Begüm Demir
In recent years, deep neural networks (DNNs) have been found very successful for multi-label classification (MLC) of remote sensing (RS) images. Self-supervised pre-training combin…
Label Noise Robust Image Representation Learning based on Supervised Variational Autoencoders in Remote Sensing
Gencer Sumbul, Begüm Demir
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). How…
Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification
David Hoffmann, Kai Norman Clasen, Begüm Demir
In this paper, we introduce a novel Synchronized Class Token Fusion (SCT Fusion) architecture in the framework of multi-modal multi-label classification (MLC) of remote sensing (RS…