Principal component analysis is a versatile statistical method for reducing a cases-by-
variables data table to its essential features, called principal components. Principal …

The globe's population is increasing day by day, which causes the severe problem of
organic food for everyone. Farmers are becoming progressively conscious of the need to …

In recent years, deep-learning techniques revolutionized the way remote sensing data are
processed. The classification of hyperspectral data is no exception to the rule, but it has …

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

In the last five years, deep learning has been introduced to tackle the hyperspectral image
(HSI) classification and demonstrated good performance. In particular, the convolutional …

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

Deep learning methods have recently been successfully explored for hyperspectral image
(HSI) classification. However, training a deep-learning classifier notoriously requires …

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications,
especially in disease diagnosis and image-guided surgery. HSI acquires a three …

In this paper, we propose a spectral–spatial unified network (SSUN) with an end-to-end
architecture for the hyperspectral image (HSI) classification. Different from traditional …

The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential
information of land objects through contiguous narrow spectral wavelength bands. The …