Anomaly detection is a hot topic in hyperspectral signal processing. The key point of
hyperspectral anomaly detection is the modeling of the background. In this paper, we …

High-dimensional change-point detection under sparse alternatives Page 1 The Annals of
Statistics 2019, Vol. 47, No. 4, 2051–2079 https://doi.org/10.1214/18-AOS1740 © Institute of …

Sparse unmixing (SU) of hyperspectral data have recently received particular attention for
analyzing remote sensing images, which aims at finding the optimal subset of signatures to …

Recently, some methods based on low-rank and sparse matrix decomposition (LRASMD)
have been developed to improve the performance of hyperspectral anomaly detection (AD) …

Hyperspectral imagery (HSI) has high spectral and spatial resolutions, which are essential
for anomaly detection (AD). Many anomaly detectors assume that the spectrum signature of …

For detecting weak and sparse signals by a set of n input p-values, the Higher Criticism (HC)
type statistics, the BerkJones (BJ) type statistics, and the phi-divergence statistics have the …

高光谱异常检测旨在识别与周围像素具有显著光谱差异的像素, 由于不需要先验光谱信息的特点
, 其在军事和民用领域发挥重要价值. 实现高光谱异常检测的一个重要手段是局部对比度计算 …

The kernel collaborative representation detector (KCRD) has desirable detection accuracy
in hyperspectral anomaly detection. Accordingly, we propose a real-time version based on …

In this study, a multiple-comparison approach is developed for detecting faint hyperspectral
sources. The detection method relies on a sparse and nonnegative representation on a …

We present SELFI, the Source Emission Line FInder, a new Bayesian method optimized for
detection of faint galaxies in Multi Unit Spectroscopic Explorer (MUSE) deep fields. MUSE is …