The rapid development of deep learning brings effective solutions for remote sensing image
interpretation. Training deep neural network models usually require a large number of …

Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …

Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …

Hyperspectral images can capture the spectral characteristics of surfaces and objects,
providing a 2-D spacial component to the spectral profiles found in a given scene. There are …

Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of
spectral bands, providing unparalleled levels of precision and accuracy in data analysis …

Convolutional neural networks (CNNs) have gained prominence in the research literature
on image classification over the last decade. One shortcoming of CNNs, however, is their …

Recently, few-shot learning (FSL) has been introduced for hyperspectral image (HSI)
classification with few labeled samples. However, existing FSL-based HSI classification …

Hyperspectral imaging has become a mature technology which brings exciting possibilities
in various domains, including satellite image analysis. However, the high dimensionality and …