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 …

Anomaly detection of hyperspectral data has been gaining particular attention for its ability in
detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants …

ABSTRACT Anomaly detection in Hyperspectral Imagery (HSI) has received considerable
attention because of its potential application in several areas. Numerous anomaly detection …

Anomaly detection has become an important remote sensing application due to the
abundant spectral and spatial information contained in hyperspectral images. Recently …

Hyperspectral image (HSI) anomaly detection (AD) generally considers background pixels
as low-rank distribution and anomaly pixels as sparse distribution. However, it is usually …

Due to the importance in many military and civilian applications, hyperspectral anomaly
detection has attracted remarkable interest. Low-rank representation (LRR)-based anomaly …

Recently, the autoencoder (AE)-based methods in hyperspectral anomaly detection (HAD)
have attracted a lot of attention from scholars and researchers, and they acquire satisfying …

In this article, a two-stream convolutional network-based target detector (denoted as
TSCNTD) for hyperspectral images is proposed. The TSCNTD utilizes the two-stream …

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 …