548 citations · 1.2k across the 34 of their papers we have counts for
29 papers · 1 filter
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…
HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods
Martin Hermann Paul Fuchs, Begüm Demir
The development of learning-based hyperspectral image compression methods has recently attracted great attention in remote sensing. Such methods require a high number of hyperspect…
Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning
Barış Büyüktaş, Gencer Sumbul, Begüm Demir
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 at…
Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning
Gencer Sumbul, Begüm Demir
The development of deep learning based image representation learning (IRL) methods has attracted great attention for various image understanding problems. Most of these methods req…
Deep Active Learning for Multi-Label Classification of Remote Sensing Images
Lars Möllenbrok, Gencer Sumbul, Begüm Demir
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 seve…
Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing
Tim Siebert, Kai Norman Clasen, Mahdyar Ravanbakhsh +1
With the new generation of satellite technologies, the archives of remote sensing (RS) images are growing very fast. To make the intrinsic information of each RS image easily acces…
Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing
Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir
The development of cross-modal retrieval systems that can search and retrieve semantically relevant data across different modalities based on a query in any modality has attracted…
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote Sensing
Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir
The development of accurate and scalable cross-modal image-text retrieval methods, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g.…
A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing
Gencer Sumbul, Markus Müller, Begüm Demir
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)…
Deep Metric Learning-Based Semi-Supervised Regression With Alternate Learning
Adina Zell, Gencer Sumbul, Begüm Demir
This paper introduces a novel deep metric learning-based semi-supervised regression (DML-S2R) method for parameter estimation problems. The proposed DML-S2R method aims to mitigate…
Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing
Georgii Mikriukov, Mahdyar Ravanbakhsh, Begüm Demir
Due to the availability of large-scale multi-modal data (e.g., satellite images acquired by different sensors, text sentences, etc) archives, the development of cross-modal retriev…
Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods
Steve Ahlswede, Nimisha Thekke-Madam, Christian Schulz +2
The collection of a high number of pixel-based labeled training samples for tree species identification is time consuming and costly in operational forestry applications. To addres…
A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-Based Retrieval
Gencer Sumbul, Jun Xiang, Nimisha Thekke Madam +1
Remote sensing (RS) images are usually stored in compressed format to reduce the storage size of the archives. Thus, existing content-based image retrieval (CBIR) systems in RS req…
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels
Ahmet Kerem Aksoy, Mahdyar Ravanbakhsh, Tristan Kreuziger +1
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in r…
BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval
Gencer Sumbul, Arne de Wall, Tristan Kreuziger +6
This paper presents the multi-modal BigEarthNet (BigEarthNet-MM) benchmark archive made up of 590,326 pairs of Sentinel-1 and Sentinel-2 image patches to support the deep learning…
A Novel Graph-Theoretic Deep Representation Learning Method for Multi-Label Remote Sensing Image Retrieval
Gencer Sumbul, Begüm Demir
This paper presents a novel graph-theoretic deep representation learning method in the framework of multi-label remote sensing (RS) image retrieval problems. The proposed method ai…
Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval
Gencer Sumbul, Mahdyar Ravanbakhsh, Begüm Demir
Learning the similarity between remote sensing (RS) images forms the foundation for content-based RS image retrieval (CBIR). Recently, deep metric learning approaches that map the…
A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification
Hichame Yessou, Gencer Sumbul, Begüm Demir
This paper analyzes and compares different deep learning loss functions in the framework of multi-label remote sensing (RS) image scene classification problems. We consider seven l…
SD-RSIC: Summarization Driven Deep Remote Sensing Image Captioning
Gencer Sumbul, Sonali Nayak, Begüm Demir
Deep neural networks (DNNs) have been recently found popular for image captioning problems in remote sensing (RS). Existing DNN based approaches rely on the availability of a train…