Life of any living being is impossible if it does not have the ability to differentiate between
various things, objects, smell, taste, colors, etc. Human being is a good ability to classify the …

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 …

Recently, low-rank representation (LRR) methods have been widely applied for
hyperspectral anomaly detection, due to their potentials in separating the backgrounds and …

Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral
data analysis. Convolutional neural networks (CNN) have been introduced to HSI …

Recent research has shown that using spectral–spatial information can considerably
improve the performance of hyperspectral image (HSI) classification. HSI data is typically …

Hyperspectral images (HSIs) have a cube form containing spatial information in two
dimensions and rich spectral information in the third one. The high volume of spectral bands …

Anomaly detection is an important task in hyperspectral remote sensing. Most widely used
detectors, such as Reed-**aoli (RX), have been developed only using original spectral …

In hyperspectral image (HSI) classification, spatial context has demonstrated its significance
in achieving promising performance. However, conventional spatial context-based methods …

Recently, hyperspectral image (HSI) classification approaches based on deep learning (DL)
models have been proposed and shown promising performance. However, because of very …

Deep learning (DL) has brought many new trends for hyperspectral image classification
(HIC). Graph neural networks (GNNs) are models that fuse DL and structured data. Although …