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 …

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 …

Learning from a limited number of labeled samples (pixels) remains a key challenge in the
hyperspectral image (HSI) classification. To address this issue, we propose a deep metric …

Abundant spectral information of hyperspectral images (HSIs) has shown an obvious
advantage in improving the performance of classification in the remote sensing domain …

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

Recently, kernel collaborative representation classification (KCRC) has shown its
outstanding performance in dealing with the problem of linear inseparability in hyperspectral …

With the rapid developments of hyperspectral imaging, the cost of collecting hyperspectral
data has been lower, while the demand for reliable and detailed hyperspectral annotations …

With the increasing popularity of deep learning models in the hyperspectral image (HSI)
classification field, more and more complex methods have been proposed. However, an …

A novel hyperspectral image (HSI) classification method based on joint collaborative
representation with shape adaptive region and locally adaptive dictionary (SALJCR) is …

Recently, representation-based classifiers have gained increasing interest in hyperspectral
image (HSI) classification. In this letter, based on our previously developed joint …