9 papers
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…
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 +1
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…
MiSiSUn: Minimum Simplex Semisupervised Unmixing
Behnood Rasti, Bikram Koirala, Paul Scheunders
This paper proposes a semisupervised geometric unmixing approach called minimum simplex semisupervised unmixing (MiSiSUn). The geometry of the data was incorporated for the first t…
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…
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…