Hyperspectral image (HSI) classification is one of the hotspots in remote sensing, and many
methods have been continuously proposed in recent years. However, it is still challenging to …

Collaborative representation (CR) models have been widely used in hyperspectral image
(HSI) classification tasks. However, most CR classification models lack stability and …

Convolutional neural networks and graph convolutional neural networks are two classical
deep learning models that have been widely used in hyperspectral image classification …

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

Nonlinear characteristics, spectral variability, and high dimensionality pose significant
challenges to the classification of hyperspectral images. Therefore, classifiers need more …

Multispectral satellites that measure the reflected energy from the different regions on the
Earth generate the multispectral (MS) images continuously. The following MS image for the …

Since multitemporal hyperspectral imaging has an excellent ability to observe the Earth's
surface over time, it has been used for various remote sensing applications. On the other …

In recent years, many target detection methods for hyperspectral images based on sparse
representation (SR) theory have been proposed and achieved good results. However, these …

Second-order statistical modeling method of depth features in image classification is studied
in this paper. Through the confrontation training of feature generators and classifiers, this …