48 citations · 61 across the 10 of their papers we have counts for
10 papers
Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagery
Tom Burgert, Leonard Hackel, Paolo Rota +1
Self-supervised learning (SSL) has become a powerful paradigm for learning from large, unlabeled datasets, particularly in computer vision (CV). However, applying SSL to multispect…
How Much of a Model Do We Need? Redundancy and Slimmability in Remote Sensing Foundation Models
Leonard Hackel, Tom Burgert, Begüm Demir
Large-scale foundation models (FMs) in remote sensing (RS) (denoted as RS FMs) are developed following paradigms established in computer vision (CV), yet the validity of transferri…
Noise-Adaptive Regularization for Robust Multi-Label Remote Sensing Image Classification
Tom Burgert, Julia Henkel, Begüm Demir
The development of reliable methods for multi-label classification (MLC) has become a prominent research direction in remote sensing (RS). As the scale of RS data continues to expa…
ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression
Tom Burgert, Oliver Stoll, Paolo Rota +1
The hypothesis that Convolutional Neural Networks (CNNs) are inherently texture-biased has shaped much of the discourse on feature use in deep learning. We revisit this hypothesis…
CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Experts
Leonard Hackel, Tom Burgert, Begüm Demir
Self-supervised learning (SSL) through masked autoencoders (MAEs) has recently attracted great attention for remote sensing (RS) foundation model (FM) development, enabling improve…
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…
reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
Kai Norman Clasen, Leonard Hackel, Tom Burgert +3
This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote sensing dataset constructed to support deep learning (DL) studies for remote sensing image…
A Label Propagation Strategy for CutMix in Multi-Label Remote Sensing Image Classification
Tom Burgert, Kai Norman Clasen, Jonas Klotz +2
The development of supervised deep learning-based methods for multi-label scene classification (MLC) is one of the prominent research directions in remote sensing (RS). However, co…
Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images
Tom Burgert, Begüm Demir
The application of data augmentation for deep learning (DL) methods plays an important role in achieving state-of-the-art results in supervised, semi-supervised, and self-supervise…
On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification
Tom Burgert, 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. To address MLC problems,…