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Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels
In this work, we consider extending classic SVMs with quantum kernels and applying them to satellite data analysis.
Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images
The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers.
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection
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.
Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines
In this work, we consider extending classical SVMs with quantum kernels and applying them to satellite data analysis.
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks
Machine learning tasks can be divided into regression and classification.
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
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.
Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation
Neural networks, in particular autoencoders, are one of the most promising solutions for unmixing hyperspectral data, i. e. reconstructing the spectra of observed substances (endmembers) and their relative mixing fractions (abundances), which is needed for effective hyperspectral analysis and classification.
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling
This approach can be applied to many of the hyperspectral classification problems.
General Classification
Hyperspectral Image Classification
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