Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Deep learning (DL) is emerging as a new tool to model spectral data acquired in analytical
experiments. Although applications are flourishing, there is also much interest currently …

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 …

Convolutional neural networks (CNNs) have recently exhibited an excellent performance in
hyperspectral image classification tasks. However, the straightforward CNN-based network …

Different forest types based on different tree species composition may have similar spectral
signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery …

Deep convolutional neural networks (CNNs) have shown their outstanding performance in
the hyperspectral image (HSI) classification. The success of CNN-based HSI classification …

Data augmentation helps improve generalization capabilities of deep neural networks when
only limited ground-truth training data are available. In this letter, we propose test-time …

Deep learning-based hyperspectral image (HSI) classification has attracted more and more
attention because of its excellent classification ability. Generally, the outstanding …

Different geographical origins can lead to great variance in coffee quality, taste, and
commercial value. Hence, controlling the authenticity of the origin of coffee beans is of great …

Although hyperspectral images capture very detailed information about the scanned objects,
their efficient analysis, transfer, and storage are still important practical challenges due to …