The deep learning-based method has shown promising competence in image classification.
Its success can be attributed to the ability to learn discriminative feature representation given …

Hyperspectral imagery (HSI) classification is one of the fundamental applications in remote
sensing domain, which aims at predicting the labels of unlabeled pixels in an image with a …

This paper studies the classification problem of hyperspectral image (HSI). Inspired by the
great success of deep neural networks in Artificial Intelligence (AI), researchers have …

Hyperspectral image (HSI) classification is a popular yet challenging research topic in the
remote sensing community. This letter attempts to encode both spectral and spatial …

Deep learning approaches have recently been widely applied to the classification of
hyperspectral images (HSIs) and achieve good capability. Deep learning can effectively …

Hyperspectral image (HSI) classification is an important topic in the community of remote
sensing, which has a wide range of applications in geoscience. Recently, deep learning …

Semisupervised deep learning methods (DLMs) can mitigate the dependence on large
amounts of labeled samples using a small number of labeled samples. However, for …

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 …

Recently, deep convolutional neural network (DCNN)-based methods have achieved much
success in hyperspectral image (HSI) classification, when sufficient labeled samples are …

In spite of being widely used in hyperspectral image (HSI) classification, most deep learning
algorithms require plenty of labeled samples to achieve satisfactory performance. However …