25 citations · 35 across the 8 of their papers we have counts for
8 papers
Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels
Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek +4
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of pattern recognition and classification tasks. In this work, we consider extendi…
Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images
Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek +4
The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers. It allows to exchange highly…
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection
Bartosz Grabowski, Maciej Ziaja, Michal Kawulok +4
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can…
Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines
Artur Miroszewski, Jakub Mielczarek, Grzegorz Czelusta +4
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with q…
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks
Bartosz Grabowski, Przemysław Głomb, Wojciech Masarczyk +4
Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-ai…
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
Bartosz Grabowski, Maciej Ziaja, Michal Kawulok +3
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can…
Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation
Kamil Książek, Przemysław Głomb, Michał Romaszewski +3
Neural networks, in particular autoencoders, are one of the most promising solutions for unmixing hyperspectral data, i.e. reconstructing the spectra of observed substances (endmem…
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling
Wojciech Masarczyk, Przemysław Głomb, Bartosz Grabowski +1
Hyperspectral imaging is a rich source of data, allowing for multitude of effective applications. However, such imaging remains challenging because of large data dimension and, typ…