Hyperspectral image (HSI) classification is one of the most important remote sensing
techniques. Currently, the performances of most of the HSI classification networks on the …

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 …

Hyperspectral image (HSI) classification accuracy has been greatly improved by employing
deep learning. The current research mainly focuses on how to build a deep network to …

Over the past few years, convolutional neural network (CNN) has been broadly adopted in
remote sensing (RS) imagery processing areas due to its impressive capabilities in feature …

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

Hyperspectral image (HSI) classification is an active and important research task driven by
many practical applications. To leverage deep learning models especially convolutional …

Recently, deep learning methods based on the combination of spatial and spectral features
have been successfully applied in hyperspectral image (HSI) classification. To improve the …

Hyperspectral image (HSI) classification is a key technique in remote sensing. Despite the
increasing availability of high-quality HSI data, obtaining a large number of labeled samples …

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 …

Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and
achieved good performance. In general, deep models adopt a large number of hierarchical …