Hyperspectral sensors enable the study of the chemical properties of scene materials
remotely for the purpose of identification, detection, and chemical composition analysis of …

This paper reviews well-known classic algorithms and more recent experimental
approaches for distinguishing the weak signal of a target (either known or anomalous) from …

Due to unavailability of prior knowledge about anomalies, background suppression (BS) is a
crucial factor in anomaly detection (AD) evaluation. The difficulty in dealing with BS arises …

In this article, a novel hyperspectral anomaly detection method with kernel Isolation Forest
(iForest) is proposed. The method is based on an assumption that anomalies rather than …

Hyperspectral target detection (HTD) and hyperspectral anomaly detection (HAD) are
designed by completely different functionalities in terms of how to carry out target detection …

A known target detection assumes that the target to be detected is provided a priori, while
anomaly detection is an unknown target detection without any prior knowledge. As a result …

Anomaly detection is one of the most important applications of hyperspectral imaging
technology. It is a challenging task due to the high dimensionality of hyperspectral images …

Limited by the anomalous spectral vectors in unlabeled hyperspectral images (HSIs),
anomaly detection methods based on background distribution estimation often suffer from …

Anomaly detection (AD) requires spectral and spatial information to differentiate anomalies
from their surrounding data samples. To capture spatial information, a general approach is …

Hyperspectral images (HSIs) with rich spectral information have been widely used in many
fields. Anomaly detection is one of the most interesting and important applications. In this …