126 citations · 552 across the 35 of their papers we have counts for
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Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation
Jakub Nalepa, Lukasz Tulczyjew, Michal Myller +1
Hyperspectral imaging provides detailed information about the scanned objects, as it captures their spectral characteristics within a large number of wavelength bands. Classificati…
Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders
Jakub Nalepa, Michal Myller, Yasuteru Imai +3
Hyperspectral image analysis has become an important topic widely researched by the remote sensing community. Classification and segmentation of such imagery help understand the un…
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
Jakub Nalepa, Pablo Ribalta Lorenzo, Michal Marcinkiewicz +8
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumor. Although manual DCE biomarker extraction algorithms…
Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images
Jakub Nalepa, Michal Myller, Michal Kawulok
Deep learning has established the state of the art in multiple fields, including hyperspectral image analysis. However, training large-capacity learners to segment such imagery req…
On training deep networks for satellite image super-resolution
Michal Kawulok, Szymon Piechaczek, Krzysztof Hrynczenko +3
The capabilities of super-resolution reconstruction (SRR)---techniques for enhancing image spatial resolution---have been recently improved significantly by the use of deep convolu…
Hyperspectral Data Augmentation
Jakub Nalepa, Michal Myller, Michal Kawulok
Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks. It plays a pivotal role in remote-sensing scenarios in which the a…