Due to the high spectral resolution, anomaly detection from hyperspectral images provides a
new way to locate potential targets in a scene, especially those targets that are spectrally …

A low-rank and sparse matrix decomposition (LRaSMD) detector has been proposed to
detect anomalies in hyperspectral imagery (HSI). The detector assumes background images …

Anomaly detection has received considerable interest for hyperspectral data exploitation
due to its high spectral resolution. A well-known algorithm for hyperspectral anomaly …

This paper proposes a randomized subspace learning based anomaly detector (RSLAD) for
hyperspectral imagery (HSI). Improved from robust principal component analysis, the …

Abstract 1 Manifold learning methods have widely used in ordinary image processing
domain. It has many advantages, depending on the different formulation of the manifold …

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

A randomized subspace-based robust principal component analysis (RSRPCA) method for
anomaly detection in hyperspectral imagery (HSI) is proposed. The RSRPCA combines …

A method of incorporating macroscopic and microscopic reflectance models into
hyperspectral pixel unmixing is presented and discussed. A vast majority of hyperspectral …

In this chapter we are presenting the literature and proposed approaches for anomaly
detection in hyperspectral images. These approaches are grouped into four categories …

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 …