104 citations · 217 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Validating Hyperspectral Image Segmentation
Jakub Nalepa, Michal Myller, Michal Kawulok
Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, hence automated approaches for precise segmentation of such imagery are being…