Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …

With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …

Despite the success of deep learning algorithms in hyperspectral image (HSI) classification,
most deep learning models require a large amount of labeled data to optimize the numerous …

With the development of hyperspectral imaging technology, hyperspectral images (HSIs)
have become important when analyzing the class of ground objects. In recent years …

In this paper, the use of Independent Component (IC) Discriminant Analysis (ICDA) for
remote sensing classification is proposed. ICDA is a nonparametric method for discriminant …

In this letter, a technique based on independent component analysis (ICA) and extended
morphological attribute profiles (EAPs) is presented for the classification of hyperspectral …

The rich information available in hyperspectral imagery not only poses significant
opportunities but also makes big challenges for material classification. Discriminative …

Deep neural networks (DNNs) have emerged as a relevant tool for the classification of
remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) …

Unsupervised hyperspectral band selection has been an important topic in hyperspectral
imagery. This technique aims at selecting some critical and decisive spectral bands from an …

Recent advances in remote sensing techniques allow for the collection of hyperspectral
images with enhanced spatial and spectral resolution. In many applications, these images …