Precision Agriculture (PA) promises to boost crop productivity while reducing agricultural
costs and environmental footprints, and therefore is attracting ever-increasing interests in …

Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover
category. In the recent past, convolutional neural network (CNN)-based HSI classification …

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) …

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

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 …

Deep neural network has been extensively applied to hyperspectral image (HSI)
classification recently. However, its success is greatly attributed to numerous labeled …

A generative adversarial network (GAN) usually contains a generative network and a
discriminative network in competition with each other. The GAN has shown its capability in a …

Due to the advantages of deep learning, in this paper, a regularized deep feature extraction
(FE) method is presented for hyperspectral image (HSI) classification using a convolutional …

So far, a large number of advanced techniques have been developed to enhance and
extract the spatially semantic information in hyperspectral image processing and analysis …