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 …

Classification is one of the most popular topics in hyperspectral remote sensing. In the last
two decades, a huge number of methods were proposed to deal with the hyperspectral data …

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great
abundance of information; such a resource, however, poses many challenges in the …

Deep learning has achieved great successes in conventional computer vision tasks. In this
paper, we exploit deep learning techniques to address the hyperspectral image …

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 learning methods have shown considerable potential for hyperspectral image (HSI)
classification, which can achieve high accuracy compared with traditional methods …

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

In recent years, deep learning based methods have attracted broad attention in the field of
hyperspectral image classification. However, due to the massive parameters and the …

Recently, networks consider spectral-spatial information in multiscale inputs less, even
though there are some networks that consider this factor, however these networks cannot …

In recent years, vector-based machine learning algorithms, such as random forests, support
vector machines, and 1-D convolutional neural networks, have shown promising results in …