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 …

In this letter, a novel spatial peak-aware collaborative representation (SPaCR) method is
proposed for hyperspectral imagery (HSI) classification, which introduces spectral–spatial …

Collaborative Representation (CR) models have been successfully employed for
Hyperspectral Images (HSIs) classification because of the effectiveness and simplicity …

Residual network which can effectively overcome gradient disappearance in convolutional
neural networks has been successfully applied to hyperspectral classification. Yet simply …

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

For hyperspectral images, the classification problem of limited training samples is an
enormous challenge, and the lack of training samples is an essential factor that affects the …

Spatial-spectral hyperspectral image classification methods have gained increasing
attention over the past decade. Although most of the methods achieve high performances in …

This work addresses the problem of hyperspectral image classification when the number of
labeled samples is very small (few shot learning). Our work is based on the recently …

Hyperspectral image (HSI) classification is a challenging problem due to the high
dimensional features, high intra-class variance, and limited prior information, and the …

In China's vast land, the mountainous environment accounts for almost 33% of the total area.
Therefore, potential mountain environmental disasters pose a great danger to daily …