Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the
last four decades from being a sparse research tool into a commodity product available to a …

Inspired by recent development of artificial satellite, remote sensing images have attracted
extensive attention. Recently, notable progress has been made in scene classification and …

Recently, the rapid development of deep learning has greatly improved the performance of
image classification. However, a central problem in hyperspectral image (HSI) classification …

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 …

In recent years, vector-based machine learning algorithms, such as random forests, support
vector machines, and 1-D convolutional neural networks, have shown promising results in …

Hyperspectral image (HSI) classification aims to assign each hyperspectral pixel with a
proper land-cover label. Recently, convolutional neural networks (CNNs) have shown …

In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper
and wider than other existing deep networks for hyperspectral image classification. Unlike …

The lack of labels is one of the major challenges in hyperspectral image (HSI) classification.
Widely used Deep Learning (DL) models such as convolutional neural networks (CNNs) …

Edge-preserving features (EPFs) obtained by the application of edge-preserving filters to
hyperspectral images (HSIs) have been found very effective in characterizing significant …

Supervised approaches classify input data using a set of representative samples for each
class, known as training samples. The collection of such samples is expensive and time …