Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …

Hyperspectral images (HSI) are obtained from hyperspectral imaging sensors to capture the
object's information in hundreds of spectral bands. However, how to make full advantage of …

A joint sparse representation (JSR) method has shown superior performance for the
classification of hyperspectral images (HSIs). However, it is prone to be affected by outliers …

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

In this paper, a self-paced joint sparse representation (SPJSR) model is proposed for the
classification of hyperspectral images (HSIs). It replaces the least-squares (LS) loss in the …

Collaborative representation is an effective way to design classifiers for many practical
applications. In this paper, we propose a novel classifier, called the prior knowledge-based …

The large spectral variability and nonlinearity of hyperspectral images (HSIs) make
classification a challenging task. Hence, the powerful capacities for feature extraction and …

Deep learning-based classification methods of hyperspectral images (HSIs) have made
significant progress recently, catching the attention of academia and industry; however, the …

In this paper, we introduce a novel classification framework for hyperspectral images (HSIs)
by jointly employing spectral, spatial, and hierarchical structure information. In this …

Recently, the capsule network and its enhanced version named convolutional capsule
network (Conv-CapsNet) were applied to hyperspectral image (HSI) classification and …