47 citations · 64 across the 13 of their papers we have counts for
14 papers
OceanMAE: A Foundation Model for Ocean Remote Sensing
Viola-Joanna Stamer, Panagiotis Agrafiotis, Behnood Rasti +1
Accurate ocean mapping is essential for applications such as bathymetry estimation, seabed characterization, marine litter detection, and ecosystem monitoring. However, ocean remot…
HyVIC: A Metric-Driven Spatio-Spectral Hyperspectral Image Compression Architecture Based on Variational Autoencoders
Martin Hermann Paul Fuchs, Behnood Rasti, Begüm Demir
The rapid growth of hyperspectral data archives in remote sensing (RS) necessitates effective compression methods for storage and transmission. Recent advances in learning-based hy…
REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent
Binger Chen, Tacettin Emre Bök, Behnood Rasti +2
Foundation Models (FMs) are increasingly integrated into remote sensing (RS) pipelines. These models include unimodal vision encoders and multimodal architectures. FMs are adapted…
Beam Index Map Prediction in Unseen Environments from Geospatial Data
Fabian Jaensch, Giuseppe Caire, Begüm Demir
In 5G, beam training consists of the efficient association of users to beams for a given beamforming codebook used at the base station and the given propagation environment in the…
Sea-Undistort: A Dataset for Through-Water Image Restoration in High Resolution Airborne Bathymetric Mapping
Maximilian Kromer, Panagiotis Agrafiotis, Begüm Demir
Accurate image-based bathymetric mapping in shallow waters remains challenging due to the complex optical distortions such as wave induced patterns, scattering and sunglint, introd…
Adjustable Spatio-Spectral Hyperspectral Image Compression Network
Martin Hermann Paul Fuchs, Behnood Rasti, Begüm Demir
With the rapid growth of hyperspectral data archives in remote sensing (RS), the need for efficient storage has become essential, driving significant attention toward learning-base…
On the Effectiveness of Methods and Metrics for Explainable AI in Remote Sensing Image Scene Classification
Jonas Klotz, Tom Burgert, Begüm Demir
The development of explainable artificial intelligence (xAI) methods for scene classification problems has attracted great attention in remote sensing (RS). Most xAI methods and th…
Continual Self-Supervised Learning with Masked Autoencoders in Remote Sensing
Lars Möllenbrok, Behnood Rasti, Begüm Demir
The development of continual learning (CL) methods, which aim to learn new tasks in a sequential manner from the training data acquired continuously, has gained great attention in…
A Plasticity-Aware Method for Continual Self-Supervised Learning in Remote Sensing
Lars Möllenbrok, Behnood Rasti, Begüm Demir
Continual self-supervised learning (CSSL) methods have gained increasing attention in remote sensing (RS) due to their capability to learn new tasks sequentially from continuous st…
Self-Supervised Cross-Modal Text-Image Time Series Retrieval in Remote Sensing
Genc Hoxha, Olivér Angyal, Begüm Demir
The development of image time series retrieval (ITSR) methods is a growing research interest in remote sensing (RS). Given a user-defined image time series (i.e., the query time se…
HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression
Martin Hermann Paul Fuchs, Behnood Rasti, Begüm Demir
The development of learning-based hyperspectral image (HSI) compression models has recently attracted significant interest. Existing models predominantly utilize convolutional filt…
Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images
Martin Hermann Paul Fuchs, Akshara Preethy Byju, Alisa Walda +2
The development of deep learning-based models for the compression of hyperspectral images (HSIs) has recently attracted great attention in remote sensing due to the sharp growing o…
Satellite Image Search in AgoraEO
Ahmet Kerem Aksoy, Pavel Dushev, Eleni Tzirita Zacharatou +5
The growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current…
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification
Ahmet Kerem Aksoy, Mahdyar Ravanbakhsh, Begüm Demir
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on…