Behnood Rasti

OceanMAE: A Foundation Model for Ocean Remote Sensing

Viola-Joanna Stamer, Panagiotis Agrafiotis, Behnood Rasti, Begüm Demir

Accurate ocean mapping is essential for applications such as bathymetry estimation, seabed characterization, marine litter detection, and ecosystem monitoring. However, ocean remote sensing (RS) remains constrained by limited labeled data and by the reduced transferability of models pre-trained mainly on land-dominated Earth …

OceanMAE: A Foundation Model for Ocean Remote Sensing

Viola-Joanna Stamer, Panagiotis Agrafiotis, Behnood Rasti, Begüm Demir

Accurate ocean mapping is essential for applications such as bathymetry estimation, seabed characterization, marine litter detection, and ecosystem monitoring. However, ocean remote sensing (RS) remains constrained by limited labeled data and by the reduced transferability of models pre-trained mainly on land-dominated Earth …

MPGU-Net: Multimodel Physics-Guided Network for Nonlinear Hyperspectral Unmixing

Xuanwen Tao, Bikram Koirala, Behnood Rasti, Antonio Plaza et autres

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REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent

Binger Chen, Tacettin Emre Bök, Behnood Rasti, Volker Markl et autres

Foundation Models (FMs) are increasingly integrated into remote sensing (RS) pipelines. These models include unimodal vision encoders and multimodal architectures. FMs are adapted to diverse perception tasks, such as image classification, change detection, and visual question answering. However, selecting the most suitable …

Adjustable Spatio-Spectral Hyperspectral Image Compression Network

Martin 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-based hyperspectral image (HSI) compression. However, a comprehensive investigation of the individual and joint effects of spectral and …

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 streams of unlabeled data. Existing CSSL methods, while learning new tasks, focus on preventing catastrophic forgetting. To this …

Low-Latency Neural Network for Efficient Hyperspectral Image Classification

Chunchao Li, Jun Li, Mingrui Peng, Behnood Rasti et autres

Hyperspectral image classification (HSIC) has been considerably improved by many lightweight and efficient networks developed to meet real-time application needs and computing resource limitations. However, theoretical floating-point operations alone are not enough to evaluate real-time quality, especially in scenarios where inference latency …

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A Spectral-Spatial Attention Network for Hyperspectral Unmixing

Xuanwen Tao, Bikram Koirala, Behnood Rasti, Antonio Plaza et autres

Hyperspectral unmixing, an essential and fundamental task in remote sensing, focuses on estimating endmembers (spectrally pure components) and their fractional abundances within each mixed pixel of a hyperspectral image. With the advent of deep learning (DL), the field of hyperspectral unmixing has …

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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 remote sensing (RS). The existing CL methods in RS, while learning new tasks, enhance …

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Adjustable Spatio-Spectral Hyperspectral Image Compression Network

Martin 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 based hyperspectral image (HSI) compression. However, a com prehensive investigation of the individual and joint effects of …

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HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression

Martin 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 filters, which capture only local dependencies. Furthermore, they often incur high training costs and exhibit substantial computational complexity. To address these limitations, in …

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Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images

Martin Fuchs, Akshara Preethy Byju, Alisa Walda, Behnood Rasti et autres

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present …

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