Hyperspectral image-anomaly detection (HSI-AD) has become one of the research hotspots
in the field of remote sensing. Because HSI's features of integrating image and spectrum …

Hyperspectral anomaly detection (HAD) is an important hyperspectral image application.
HAD can find pixels with anomalous spectral signatures compared with their neighbor …

Hyperspectral imagery can obtain hundreds of narrow spectral bands of ground objects. The
abundant and detailed spectral information offers a unique diagnostic identification ability for …

Hyperspectral imaging is an essential imaging modality for a wide range of applications,
especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral …

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, we propose a weakly supervised low-rank representation (WSLRR) method for
hyperspectral anomaly detection (HAD), which formulates deep learning-based HAD into a …

Band selection (BS) is a commonly used dimension reduction technique for hyperspectral
images. In this article, we propose a similarity-based ranking (SR) strategy inspired by a …

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

Anomaly detection in hyperspectral images (HSIs) faces various levels of difficulty due to the
high dimensionality, redundant information and deteriorated bands. To address these …

Sparse representation (SR)-based approaches and collaborative representation (CR)-
based methods are proved to be effective to detect the anomalies in a hyperspectral image …