Compared to natural images, hyperspectral images (HSIs) consist of a large number of
bands, with each band capturing different spectral information from a certain wavelength …

Hyperspectral anomaly detection (HAD) has always been a hot topic in hyperspectral image
(HSI) processing. The Reed-Xiaoli (RX) detector is one of the most widely used methods for …

High-dimensional data is characterized by its sparsity and noise, which can increase the
likelihood of overfitting and compromise the model's generalizability performance. In this …

This work is devoted to the investigation of dimension reduction problem. As an efficient
dimension reduction method, much attention has been paid on unsupervised subspace …

Hyperspectral anomaly detection (HAD) entails identifying anomaly pixels in hyperspectral
images (HSI) that significantly diverge from the background spectral signatures. However …

Anomaly detection in hyperspectral imaging is crucial for remote sensing, driving the
development of numerous algorithms. However, systematic studies reveal a dichotomy …

Hyperspectral images include information from a wide range of spectral bands deemed
valuable for computer vision applications in various domains such as agriculture …

Clustering is used widely in various kinds of signal processing tasks, in which-means is
warmly welcomed by the researchers due to its efficiency and simplicity. Nevertheless, it fails …

Hyperspectral anomaly detection is an alluring topic in hyperspectral image processing. As
one of the most famous hyperspectral anomaly detection algorithms, Reed-Xiaoli detector is …

In the acquisition process of hyperspectral images (HSIs), each band may be contaminated
with different degrees of mixing noise. For hyperspectral anomaly detection (HAD) tasks …