Hyperspectral image (HSI) classification has become a hot topic in the field of remote
sensing. In general, the complex characteristics of hyperspectral data make the accurate …

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 …

Convolutional neural networks (CNNs) have been attracting increasing attention in
hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …

Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …

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

Recently, a great many deep convolutional neural network (CNN)-based methods have
been proposed for hyperspectral image (HSI) classification. Although the proposed CNN …

Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …

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 …

Hyperspectral imagery can obtain hundreds of narrow spectral bands of ground objects. The
abundant and detailed spectral information offers a unique diagnostic identification ability for …

By considering the spectral signature as a sequence, recurrent neural networks (RNNs)
have been successfully used to learn discriminative features from hyperspectral images …